Annotating data entails applying prepared labels or annotations to the data in accordance with the task at hand. During the training phase, the machine learning model draws on these annotations as the “ground truth” or “reference points.” Data annotation is important for supervised learning as it offers the necessary information for the model to generalize relationships and patterns within the data.

Data annotation in machine learning involves the process of labeling or tagging data with relevant information, which is used to train and improve the accuracy of machine learning algorithms. 

Different kinds of machine learning tasks need specific kinds of data annotations. Here are some important tasks to consider: 

Classification 

For tasks like text classification, sentiment analysis, or image classification, data annotators assign class labels to the data points. These labels indicate the class or category to which each data point belongs. 

Object Detection 

For tasks involving object detection in images or videos, annotators mark the boundaries and location of objects in the data along with assigning the necessary labels. 

Semantic Segmentation 

In this task, each pixel or region of an image is given a class label allowing the model to comprehend the semantic significance of the various regions of an image.

Sentiment Analysis 

In sentiment analysis, sentiment labels (positive, negative, neutral) are assigned by annotators to text data depending on the expressed sentiment.

Speech Recognition 

Annotators translate spoken words into text for speech recognition tasks, resulting in a dataset that combines audio with the appropriate text transcriptions.

Translation 

For carrying out machine translation tasks, annotators convert text from one language to another to provide parallel datasets.

Named Entity Recognition (NER) 

Annotators label particular items in a text corpus, such as names, dates, locations, etc., for tasks like NER in natural language processing.

Data annotation is generally performed by human annotators who follow particular instructions or guidelines provided by subject-matter experts. To guarantee that the annotations appropriately represent the desired information, quality control, and consistency are crucial. The need for correct labeling sometimes necessitates domain-specific expertise as models get more complex and specialized.

Data annotation is a crucial stage in the machine learning pipeline since the dependability and performance of the trained models are directly impacted by the quality and correctness of the annotations.

Significance of Quality Data Annotation for Machine Learning Models

In order to comprehend how quality data annotation affects machine learning model performance, it is important to consider several important elements. Let's consider those: 

Training Data Quality 

The quality of training data is directly impacted by the quality annotations. Annotations of high quality give precise and consistent labels, lowering noise and ambiguity in the dataset. Annotations that are not accurate can lead to model misinterpretation and inadequate generalization to real-world settings.

Bias Reduction

An accurate data annotation assists in locating and reducing biases in the dataset. Biased models may produce unfair or discriminatory predictions as a result of biased annotations. Before training the model, researchers can identify and correct such biases with the help of high-quality data annotation.

Model Generalization

A model is better able to extract meaningful patterns and correlations from the data when the dataset is appropriately annotated using data annotation services. By assisting the model in generalizing these patterns to previously unexplored data, high-quality annotations enhance the model's capacity to generate precise predictions about new samples.

Decreased Annotation Noise

Annotation noise i.e. inconsistencies or mistakes in labeling is diminished by high-quality annotations. Annotation noise might be confusing to the model and have an impact on how it learns. The performance of the model can be improved by maintaining annotation consistency.

Improved Algorithm Development

For machine learning algorithms to work successfully, large amounts of data are frequently needed. By utilizing the rich information present in precisely annotated data, quality annotations allow algorithm developers to design more effective and efficient models.

Efficiency of Resources

By decreasing the need for model training or reannotation owing to inconsistent or incorrect models, quality annotations help save resources. This results in faster model development and deployment. 

Domain-Specific Knowledge

Accurate annotation occasionally calls for domain-specific knowledge. Better model performance in specialized areas can be attained by using high-quality annotations to make sure that this knowledge is accurately recorded in the dataset.

Transparency and Comprehensibility

The decisions made by the model are transparent and easier to understand when annotations are accurate. This is particularly significant for applications, such as those in healthcare and finance, where comprehending the logic behind a forecast is essential.

Learning and Fine-Tuning

High-quality annotations allow pre-trained models to be fine-tuned on domain-specific data. By doing this, the model performs better on tasks related to the annotated data.

Human-in-the-Loop Systems

Quality annotations are crucial in active learning or human-in-the-loop systems where models iteratively request annotations for uncertain cases. Inaccurate annotations can produce biased feedback loops and impede the model's ability to learn.

Benchmarking and Research

Annotated datasets of high quality can serve as benchmarks for assessing and comparing various machine-learning models. This quickens the pace of research and contributes to the development of cutting-edge capabilities across numerous sectors.

Bottom Line

The foundation of a good machine learning model is high-quality data annotation. The training, generalization, bias reduction, and overall performance of a model are directly influenced by accurate, dependable, and unbiased annotations. For the purpose of developing efficient and trustworthy machine learning systems, it is essential to put time and effort into acquiring high-quality annotations.

The post The Impact of Quality Data Annotation on Machine Learning Model Performance appeared first on Datafloq.

To avoid being in a similar situation, always ask your enterprise software solutions vendor for a proof of concept – especially if your company is just testing a new technology or methodology.

So, what is a proof of concept in software development? Who needs it? And how to go through PoC implementation?

What does PoC in software development mean?

Proof of concept in software development is a verification methodology that helps you test the feasibility of your software idea on a smaller scale. It aims to prove that the solution can be built, actually work in real life, solve existing pain points, and yield financial gain.

PoC can take place at any stage of the software development life cycle. You can conduct it in the very beginning to test the viability of the entire idea, or you can resort to it halfway through the project to test a particular feature. For instance, you might want to add artificial intelligence capabilities to the solution under development. So, you continue with the original project as planned and conduct a separate PoC to test the new AI feature. You can find more information on this topic in our article on AI PoC.

Proof of concept deliverables in software development can take different forms, including a document, presentation, written code, etc.

After executing a PoC, you will have a better understanding of whether your software idea has merits. Additionally, you will have a more clear view on the following:

• Which challenges you can anticipate during the implementation
• What risks and limitations the product entails
• How it functions
• Which technology is best suited for the development
• Which other benefits, that you haven't initially considered, this solution can offe
• How much it will cost to build the final product
• How long it will take to finish the application

People tend to confuse a proof of concept with a prototype and a minimum viable product (MVP), but these are different concepts, each one resulting in its own unique deliverables. Let's see how these concepts differ from each other.

PoC vs prototype

While a proof of concept in software development aims to validate the idea behind an application, a prototype assumes that the idea is viable and aims to test a specific implementation of this idea. It shows how the final product will look, which functionality it will include, and how to use it. A prototype displays the general look and feel of the application and shows how to access the functionality, without necessarily having all the functionality already implemented.

A prototype can take different forms, such as wireframes, clickable mockups, etc. You can show the prototype to your prospective clients to get their feedback on the visuals. Therefore, UX designers are heavily involved during the prototyping stage, while a PoC can still serve its purpose with a poor user interface.

PoC vs MVP

A minimum viable product is the next step after a prototype. It's the simplest working, market-ready version of your product that covers all the essential functionality. You can release an MVP to the general public to buy and use.

Unlike a prototype, which might not be fully functioning, an MVP offers the basic functionality, which actually works and provides value to the end users. It's introduced to the market to see if people are willing to use the product and to gather feedback from early adopters for the next improvement iterations. This step helps you understand if the target audience is ready for your product before you invest even more resources in a full-fledged solution that no one will end up buying.

Benefits of PoC in software development

Research shows that only 14% of software development projects are completed successfully.

So, what can you do to improve your chances? First of all, it makes sense to validate whether your product idea is feasible from the technical and financial perspectives. This is what a PoC can tell you in a rather short amount of time. And here are other benefits of opting for a proof of concept in software development:

• Getting some sort of feasibility proof that you can show to potential investors
• Understanding the limitations of such a product
• Identifying potential risks at the early stage and finding a way to mitigate them
• Preparing a more accurate budget estimation
• Accelerating the final product release

When PoC is a must, and when you can move forward without it

Proof of concept in software development is not limited to a particular industry. And contrary to popular belief that PoC is only applicable to startups, enterprises of any size can benefit from this methodology to evaluate their ideas.

Does it mean that a proof of concept stage has to be a part of every software development project? Let's see.

When is PoC in software development an absolute must?

• If your project relies on an innovative idea that was not tested yet
• If you're not sure whether the idea will work
• If you want to test a new technology or methodology before implementing it on a large scale
• When time to market is of utmost importance
• When you need to convince investors to fund innovative technology
• To test the efficiency and viability of a solution that you want to patent

And when can you skip a PoC and go straight to an MVP or a full-fledged project?

• If the software you want to develop is rather standard and resembles common practices in the field, such as building yet another eCommerce website
• If your idea relies on a technology that your engineers and developers understand very well
• When making minor changes to existing software
• When working on a project with meticulously documented requirements

A step-by-step guide through PoC in software development

After learning what PoC in software development is and when to use it, let's go through the implementation process.

Proof of concept in software development is like any other project, with the difference that you can terminate it or pivot at any point when you discover that the idea behind it isn't feasible. And you can iterate on one step as many times as needed.

Below you will find the five steps that ITRex takes while working on PoC projects. Please note that the PoC team can go through these steps either for the whole PoC scope or for each feature independently.

To clarify what each PoC implementation step entails, we will use an artificial PoC example throughout this section.

Here is the description of this fictional project:

A US-based company operating in wholesale and retail has around 10,000 partners, which results in a high sales orders (SOs) and purchase orders (POs) processing load. The company's operations are geographically limited to the US, and it doesn't have its own delivery system. The firm receives a large number of paper-based SOs and POs daily. Some arrive as PDF files, some as fax, and sometimes orders are placed through a phone call. All POs and SOs are processed manually.

This company is looking to partially or fully automate order processing to take the load off its employees and reduce costs.

They want to conduct a PoC to verify if it's possible to automate PO and SO document processing to support order handling.

Let's go through the PoC steps together to see how the methodology works.

Step 1: Define the scope

When a client comes to ITRex with a PoC idea, we work on defining the scope to prevent it from endlessly expanding. We can do this using interview techniques, questionnaires, or even resort to on-site observations. During this step, we aim to understand and document the current state of affairs and the desired future situation.

In the wholesale company's proof of concept in software development example, the PoC team will try to understand the current state of affairs by asking questions, such as:

• What are the data transport and consumption pipeline(s)?
• In which formats do you currently receive your PO and SO documents?
• What is the ratio of different formats (carbon copy, fax, email, etc.) for the POs and SO?
• Should you import it directly into your ERP system?
• How much data (address, PO/SO number, UPC, etc.) one uses from a single PO or SO throughout the whole processing routine?
• What data may be dictionarised for further automation?
• How much time do you spend on manual order processing?

The PoC team will then work together with the company to determine what they want to achieve. They can come up with the following list of features:

Feature 1: Converting all paper-based documents into electronic form and storing them all in one location

Feature 2: Automatically processing the electronic documents with optical character recognition (OCR) to extract relevant data

Feature 3: Analyzing and manipulating the extracted data

Feature 4: Feeding the extracted order data into the company's ERP system

At this stage, we have a list of features, but we didn't specify how to implement them yet. Let's move to the next step to clarify this.

Step 2: Define the solution's acceptance and success criteria

During this step, we will get specific about what we want to achieve and how to evaluate it by writing down acceptance and success criteria.

• Acceptance criteria are conditions that the PoC application has to meet to be submitted to the client
• Success criteria refer to what makes the PoC project a success in terms of supporting the hypothesis

At ITRex, we make sure that all the criteria are specific, measurable, achievable, realistic, and timely (SMART). And of course, approved by the client.

Coming back to the wholesale PoC project, the client estimated that 62% of all POs and SOs arrive as PDF files, 29% are sent over fax, 5% are transmitted as images, and the remaining amount is coming through phone calls. Consequently, the company decided to focus on PDF and fax and ignore the rest for the time being.

The PoC team proposed implementing an AI solution to transcribe phone calls, but given that these calls constitute only a small percentage of the PO and SO bulk, and this solution would be rather expensive, the client decided against it. You can find more information on costs associated with AI implementation on our blog.

Here are a few examples of acceptance and success criteria for this project:

Acceptance criteria:

• POs and SOs arriving as printed PDFs are converted into electronic format upon successful recognition
• Unrecognized documents are stored in the designated location
• A notification mail is sent to the designated user on each unsuccessful document recognition case

Success criteria:

• 70% of the physical PO and SO documents can be converted into electronic format. This includes PDFs, fax, image files, etc.
• All electronic documents can be integrated with the company's ERP system
• The selected OCR algorithm hits an 85% precision rate in data extraction
• Order handling time is reduced from 30 minutes when done manually to 10 minutes after automating document processing

Step 3: Select the tech stack

When it comes to choosing the right technology, in a nutshell, consider three main factors – speed, budget, and reliability. This will help you decide whether to purchase an off-the-shelf product or build a custom solution.

There are other important aspects to consider, such as compliance for industries like pharma.

In our wholesale company example, the PoC team decides to use an open-source OCR solution to save time and money and rely on AWS cloud storage to maintain the electronic version of sales and purchase orders. And, they will deploy the ready-made eFax solution to receive faxes in electronic format.

Step 4: Build and test the PoC application

During this step, the actual PoC application is built based on the features and the acceptance criteria identified above. Keep in mind that during PoC in software development, you can stop at any time if you have enough evidence that the solution is not feasible. At ITRex, we use time and materials (T&M) contracts for PoCs, which gives you the freedom to terminate a PoC project whenever you want without overpaying.

If you decide to move towards PoC implementation, our team will create the assignment backlog, set deadlines, decide on the team's composition, and begin implementing the agreed-upon features. Next, we perform PoC application quality assurance to validate it against the acceptance criteria, detect any bugs, fix them if needed, or just document them for future reference if their presence doesn't interfere with PoC hypothesis verification. And finally, we will present a demo that you can evaluate and give your feedback.

If you are interested in performing user acceptance testing, we can assist you with that as well.

Coming back to the wholesale company, the PoC team will implement the four features highlighted in the Define the scope section and test the resulting application against the acceptance criteria.

Step 5: Evaluate the results and decide on the next step

You assess whether the success criteria are met and decide if they are still relevant. If the PoC solution did not meet the expectations, our team will prepare a hypothesis failure report explaining why the PoC idea in its current form is not viable.

If you are satisfied with the results, you can use our MVP development services or start preparing for the full-fledged project. And in the case when the PoC application's performance wasn't up to par but you aren't ready to give up on the idea just yet, we can make improvements, redefine success criteria, or put forward a new hypothesis, and iterate on the PoC implementation process.

Proof of concept in software development examples from ITRex portfolio

Here are three examples from our portfolio that highlight PoC benefits and show what can happen if a company decides to skip the proof of concept stage.

Crawling social media for sentiment analysis

The area of operations

Entertainment

Project description

The customer wanted to build an AI-powered analytics platform that musicians can use to gauge people's sentiment toward them. This solution would crawl social media platforms, gather data, and process it to extract sentiment. Musicians who decide to sign up with this platform will receive information on how people perceive them, and which social media behavior will attract the most attention.

As we started working on the proof of concept, we realized that due to restrictions enforced by Meta, it was impossible to extract the needed data from Facebook and Instagram to use for commercial purposes. And the client failed to provide their verified business account on Meta, which was a prerequisite for retrieving data via Graph API.

Benefits achieved through a PoC

The client only spent $5,000 on the proof of concept until it became clear this idea wasn't viable. If the customer would have decided to skip the PoC, they would have wasted $20,000 more on the discovery project.

How a client skipped the PoC and was left with an unusable solution

The area of operations

Gambling and advertisement

Project description

An entrepreneur wanted to build a mobile app that would play different vendors' ads and randomly display a button, prompting the audience to claim their reward. If you manage to click the button before it disappears, you will be entitled to a monetary amount.

The entrepreneur hired an outsourcing company that proceeded to develop the apps without testing the idea's feasibility first. When both Android and iOS solutions were ready, the client was horrified to discover that due to technical issues with ad streaming, users couldn't clearly view and press the button in time to claim their reward, rendering the whole setup unusable.

The result of skipping PoC

The client spent over $70,000 to end up with two apps (Android and iOS) that the target audience can't use. He wouldn't have lost all this money if the vendor had suggested starting the project with a proof of concept.

What we did

This client was devastated when he turned to ITRex. Our team conducted a PoC to experiment with different user flows. As a result, we came up with a flow that wasn't impacted by latency and poor connectivity, allowing users to view the ads and press the reward button within the needed time frame.

Automating post-clinical care and recovery

The area of operations

Healthcare

Project description

A company operating in the healthcare sector wanted to build a solution that automates post-clinical care and recovery processes. This product is supposed to automatically generate detailed recovery plans that patients can use in insurance claims. This solution would also support patients in scheduling follow-up appointments with the right healthcare provider and connect with EHR systems of different hospitals to distribute questionnaires to patients.

The firm was planning to sell this product to hospitals and patients but wanted to test the viability of this idea first.

For this proof of concept in software development, the client wanted to build something cheap but still workable. Initially, they suggested using a specific EMR solution, but after thorough research, we suggested a more cost-effective alternative. We also skipped the automation part and provided recovery plans manually, while questionnaires were sent to patients through emails. This was a rather inexpensive setup to prove that this idea can work.

Benefits achieved through the PoC

As a result, the client could verify the viability of their idea while spending less, as we suggested an alternative to their proposed EMR system. In general, the client didn't have to spend time researching the issue on their own. They just brought in the idea, and our team researched it further.

Tips for a successful PoC implementation

Here are some tips that will help you sail through PoC in software development:

• Keep the proof of concept simple so that you can finish it in a reasonable timeframe
• Clearly define what success means to you
• Make sure the technical staff members understand the success criteria
• If you are conducting a PoC to convince investors to fund your project, make sure the language you use is understandable for people with no coding experience
• Involve key stakeholders, even though this is just a hypothesis verification phase. Let them experiment with the solution and witness its benefits firsthand
• The client and the team should understand the strategic values behind this project
• Make sure the PoC team is diverse and not limited to developers. You may want to include a business analyst and a QA engineer
• Always trust your tech lead regarding infrastructure and implementation tools
• Nobody is to blame for the failed hypothesis. It's not the same as a failed project

To summarize

Proof of concept in software development will help you test the viability of your idea, understand product limitations and risks, calculate the budget with precision, and more.

PoC is not limited to startups. Large enterprises can also benefit from this methodology, especially if they want to experiment with innovative technologies like ML, IPA, and IoT.

At ITRex, we approach all PoC projects with efficiency and reusability in mind. As a result, our teams reuse approximately 40-45% of the PoC's architecture and code base. For the sake of context, the median reusability rate in the IT industry is around 10%. With our savvy approach, a PoC in software development will not only help you prove the viability of your idea, but will also get you started with building the final product. If our team encounters any feasibility-threatening issues, we immediately bring that to your attention and discuss potential solutions, or stop if you don't want to take this risk.

The post What Is Proof of Concept in Software Development appeared first on Datafloq.

DevOps has evolved, and this essay will study its current condition, explore upcoming DevOp trends, and make some predictions about DevOps‘ future in 2023.

What is DevOps?

DevOps is a set of practices, tools, and cultural principles that can automate and integrate software development processes and operations. It is a software development method that focuses on interaction, coordination, and integration between programmers and IT staff.

The Origin of DevOps

The term “DevOps” first appeared in the middle of the 2000s, when businesses realized they needed a better approach to delivering software. The conventional software development method was proving to be excessively slow and complicated. Because different departments handled operations and development, this strategy's segmented structure caused poor communication and collaboration. As a result, it took lengthy lead times, sluggish feedback cycles, and subpar software.

Or this reason, a fresh strategy was required to address these problems, and DevOps was created. In order to improve communication, cooperation, and integration, DevOps sought to bring together software development and operations. The emphasis was on accelerating delivery times, cutting lead times, and automating manual operations.

The Current State of DevOps

DevOps has come a long way since its inception a decade ago. Organizations have recognized the value of DevOps in accelerating software delivery and improving customer satisfaction. DevOps has also proven to be a critical component in digital transformation initiatives for businesses across industries. According to a recent study by Gartner, 80% of organizations that have adopted DevOps have experienced improved software delivery and customer satisfaction.

Moreover, According to a recent survey, the DevOps market will exceed $20 billion by 2026, growing at a CAGR of 24.7% from 2019 to 2026. DevOps has facilitated rapid and dependable software development, delivery, improved quality and higher customer satisfaction.

DevOps practices have also expanded beyond the traditional software development and operations domains. Today, DevOps includes testing, security, and other essential functions for delivering high-quality software. DevSecOps, a practice that combines development, security, and operations, is gaining popularity as security becomes an integral part of the software development lifecycle.

The Top DevOps Trends in 2023

Over the last decade, DevOps has undergone significant changes. Modern-day DevOps goes beyond automating tasks or relying on developers to write process scripts. It is a culture that emphasizes improving business outcomes by adopting DevOps practices. Looking ahead, the success of DevOps will hinge on improved communication and increased job opportunities.

As we move deeper into 2023, we can see several trends shaping the future of DevOps, and the landscape is continually evolving. Below are some of the emerging technologies and methodologies that are likely to have a significant impact on the next chapter of DevOps.

Kubernetes

One of the most intriguing DevOps trends is Kubernetes. According to Datadog's 2022 survey, Kubernetes (K8s) was the preferred technology for deploying and managing containerized environments in nearly 50% of surveyed organizations. Additionally, IBM's research found that approximately 85% of container users experienced increased productivity due to benefits such as source control, automated scaling, and the ability to reuse code across systems.

Kubernetes is a widely-supported container orchestration platform developed by Google and backed by major cloud providers such as Amazon Web Services, Microsoft Azure DevOps, and Oracle Cloud. Being open-source, it boasts an active community that regularly introduces new add-ons to extend its functionality.

Cloud-Native Environments

In 2023, DevOps teams are expected to continue adopting serverless or cloud-native environments hosted by third-party providers. This approach removes the need for companies to invest in expensive hardware purchases, configuration, and maintenance. Instead, cloud providers handle server management, infrastructure scaling, and resource provisioning.

Using a serverless environment allows developers to avoid the tedious aspects of system maintenance, while companies can save time and money. Cloud-native technology relies on microservices, containers, and immutable infrastructure, which offers several advantages to DevOps practitioners. This approach enables faster iteration by reducing dependencies on a single application or service. Additionally, immutable infrastructure allows developers to deploy changes without disrupting production services.

AIOps

AIOps is a combination of artificial intelligence (AI) and operations that aims to automate the management and monitoring of IT systems. AI-powered software can identify coding errors, predict issues, optimize code, and automate the management and monitoring of IT systems. Some mature organizations experiment with algorithmic models that detect inefficient coding practices and offer suggestions for optimization. With AIOps, DevOps practitioners can identify and troubleshoot issues before they impact end-users. In 2023, we can expect to see more organizations adopting AIOps to improve the speed and quality of their software delivery.

GitOps

GitOps is a practice that uses Git as the single source of truth for defining infrastructure and application deployments. In a GitOps environment, all changes to the infrastructure and applications are made through Git commits. GitOps is among the DevOps trends that can help organizations reduce complexity, improve visibility, and increase the speed of deployment.

Furthermore, with GitOps, network configuration, storage, and deployment environments are automatically optimized. DevOps teams benefit from this by receiving infrastructure updates that are always optimized for continuous deployment. In addition, cross-functional teams can access common standards and vocabulary.

Predictions for the Future of DevOps Trends

DevOps has been shaped by a range of tools and practices that have emerged over the past decade, with some looking to the future while others are more established. Regardless, their common goal is delivering software products faster, cost-effectively, and with greater security.

With the future of DevOps looking bright, we can expect significant changes in the DevOps landscape as we dive deeper into 2023. In anticipation, here are some predictions for the future of DevOps:

Increased Automation

The role of automation in DevOps will continue to grow as organizations seek to improve the speed, consistency, and quality of software development. Automation can reduce errors, and the software development lifecycle can be sped up.

Emphasis on Data-Driven Practices

DevOps generates significant amounts of data, and we can expect to see more organizations utilizing data-driven practices to optimize their DevOps processes, identify trends, and improve software delivery quality.

More Focus on Security

Security is becoming increasingly important in the software development lifecycle, and organizations will continue to adopt DevSecOps practices to integrate security into the development process, identify security risks early on, and ensure compliance with security standards.

Enhanced Collaboration

Collaboration between development, operations, and other teams involved in software development will become more common as organizations seek to break down silos and improve overall efficiency and effectiveness.

Platform-agnostic Approach

DevOps practices will become more flexible and adaptable to different platforms and technologies as organizations adopt multi-cloud and hybrid cloud environments. It will require DevOps teams to be more versatile and able to work with a range of technologies.

Conclusion

The evolution of DevOps has been marked by continuous innovation and adaptation to meet the evolving needs of software development and operations. Several key DevOps trends have emerged, such as the integration of AI and machine learning, the rise of serverless architectures, and the growing importance of security and compliance. Looking ahead, the future of DevOps holds exciting possibilities, including further automation, increased adoption of DevSecOps practices, and the exploration of new technologies like edge computing and quantum computing. As organizations strive for greater efficiency, speed, and resilience in their software delivery, DevOps will continue to play a crucial role in shaping the future of software development and operations.

The post The Evolution of DevOps: Trends and Predictions for the Future appeared first on Datafloq.

You can avoid problems like this if you pay attention to software scalability during the early stages of development, whether you implement it yourself or use software engineering services.

So, what is scalability in software? How to make sure your solution is scalable? And when do you need to start scaling?

What is software scalability?

Gartner defines scalability as the measure of a system's ability to decrease or increase in performance and cost in response to changes in processing demands.

In the context of software development, scalability is an application's ability to handle workload variation while adding or removing users with minimal costs. So, a scalable solution is expected to remain stable and maintain its performance after a steep workload increase, whether expected or spontaneous. Examples of increased workload are:

• Many users accessing the system simultaneously
• Expansion in storage capacity requirements
• Increased number of transactions being processed

Software scalability types

You can scale an application either horizontally or vertically. Let's see what the benefits and the drawbacks of each approach are.

Horizontal software scalability (scaling out)

You can scale software horizontally by incorporating additional nodes into the system to handle a higher load, as it will be distributed across the machines. For instance, if an application starts experiencing delays, you can scale out by adding another server.

Horizontal scalability is a better choice when you can't estimate how much load your application will need to handle in the future. It's also a go-to option for software that needs to scale fast with no downtime.

Source

Benefits:

• Resilience to failure. If one node fails, others will pick up the slack
• There is no downtime period during scaling as there is no need to deactivate existing nodes while adding new ones
• Theoretically, the possibilities to scale horizontally are unlimited

Limitations:

• Added complexity. You need to determine how the workload is distributed among the nodes. You can use Kubernetes for load management
• Higher costs. Adding new nodes costs more than upgrading existing ones
• The overall software speed might be restricted by the speed of node communication

Vertical software scalability (scaling up)

Vertical scalability is about adding more power to the existing hardware. If with horizontal scalability you would add another server to handle an application's load, here you will update the existing server by adding more processing power, memory, etc. Another option is removing the old server and connecting a more advanced and capable one instead.

This scalability type works well when you know the amount of extra load that you need to incorporate.

Source

Benefits:

• There is no need to change the configuration or an application's logic to adapt to the updated infrastructure
• Lower expenses, as it costs less to upgrade than to add another machine

Limitations:

• There is downtime during the upgrading process
• The upgraded machine still presents a single point of failure
• There is a limit on how much you can upgrade one device

Vertical vs. horizontal scalability of software

When do you absolutely need scalability?

Many companies sideline scalability in software engineering in favor of lower costs and shorter software development lifecycles. And even though there are a few cases where scalability is not an essential system quality attribute, in most situations, you need to consider it from the early stages of your product life cycle.

When software scalability is not needed:

• If the software is a proof of concept (PoC) or a prototype
• When developing internal software for small companies used only by employees
• Mobile/desktop app without a back end

For the rest, it's strongly recommended to look into scalability options to be ready when the time comes. And how do you know it's time to scale? When you notice performance degradation. Here are some indications:

• Application response time increases
• Inability to handle concurrent user requests
• Increased error rates, such as connection failures and timeouts
• Bottlenecks are forming frequently. You can't access the database, authentication fails, etc.

Tips for building highly scalable software

Software scalability is much cheaper and easier to implement if considered at the very beginning of software development. If you have to scale unexpectedly without taking the necessary steps during implementation, the process will consume much more time and resources. One such approach is to refactor the code, which is a duplicate effort, as it doesn't add any new features. It simply does what should have been done during development.

Below you can find eight tips that will help you build software that is easier to scale in the future. The table below divides the tips into different software development stages.

Tip #1: Opt for hosting in the cloud for better software scalability

You have two options to host your applications, either in the cloud or on premises. Or you can use a hybrid approach.

If you opt for the on-premises model, you will rely on your own infrastructure to run applications, accommodate your data storage, etc. This setup will limit your ability to scale and make it more expensive. However, if you operate in a heavily regulated sector, you might not have a choice, as on-premises hosting gives you more control over the data.

Also, in some sectors, such as banking, transaction handling time is of the essence and you can't afford to wait for the cloud to respond or tolerate any downtime from cloud providers. Companies operating in these industries are restricted to using specific hardware and can't rely on whatever cloud providers offer. The same goes for time-sensitive, mission-critical applications, like automated vehicles.

Choosing cloud computing services will give you the possibility to access third-party resources instead of using your infrastructure. With the cloud, you have an almost unlimited possibility to scale up and down without having to invest in servers and other hardware. Cloud vendors are also responsible for maintaining and securing the infrastructure.

If you are working in the healthcare industry, you can check out our article on cloud computing in the medical sector.

Tip #2: Use load balancing

If you decide to scale horizontally, you will need to deploy load-balancing software to distribute incoming requests among all devices capable of handling them and make sure no server is overwhelmed. If one server goes down, a load balancer will redirect the server's traffic to other online machines that can handle these requests.

When a new node is connected, it will automatically become a part of the setup and will start receiving requests too.

Tip #3: Cache as much as you can

Cache is used to store static content and pre-calculated results that users can access without the need to go through calculations again.

Cache as much data as you can to take the load off your database. Configure your processing logic in a way that data which is rarely altered but read rather often can be retrieved from a distributed cache. This will be faster and less expensive than querying the database with every simple request. Also, when something is not in the cache but is accessed often, your application will retrieve it and cache the results.

This brings issues, such as, how often should you invalidate the cache, how many times a piece of data needs to be accessed to be copied to the cache, etc.

Tip #4: Enable access through APIs

End users will access your software through a variety of clients, and it will be more convenient to offer an application programming interface (API) that everyone can use to connect. An API is like an intermediary that allows two applications to talk. Make sure that you account for different client types, including smartphones, desktop apps, etc.

Keep in mind that APIs can expose you to security vulnerabilities. Try to address this before it's too late. You can use secure gateways, strong authentication, encryption methods, and more.

Tip #5: Benefit from asynchronous processing

An asynchronous process is a process that can execute tasks in the background. The client doesn't need to wait for the results and can start working on something else. This technique enables software scalability as it allows applications to run more threads, enabling nodes to be more scalable and handle more load. And if a time-consuming task comes in, it will not block the execution threat, and the application will still be able to handle other tasks simultaneously.

Asynchronous processing is also about spreading processes into steps when there is no need to wait for one step to be completed before starting the next one if this is not critical for the system. This setup allows distributing one process over multiple execution threads, which also facilitates scalability.

Asynchronous processing is achieved at the code and infrastructure level, while asynchronous request handling is code level.

Tip #6: Opt for database types that are easier to scale, when possible

Some databases are easier to scale than others. For instance, NoSQL databases, such as MongoDB, are more scalable than SQL. The aforementioned MongoDB is open source, and it's typically used for real-time big data analysis. Other NoSQL options are Amazon DynamoDB and Google Bigtable.

SQL performs well when it comes to scaling read operations, but it stalls on write operations due to its conformity to ACID principles (atomicity, consistency, isolation, and durability). So, if these principles aren't the main concern, you can opt for NoSQL for easier scaling. If you need to rely on relational databases, for consistency or any other matter, it's still possible to scale using sharding and other techniques.

Tip #7: Choose microservices over monolith architecture, if applicable

Monolithic architecture

Monolithic software is built as a single unit combining client-side and server-side operations, a database, etc. Everything is tightly coupled and has a single code base for all its functionality. You can't just update one part without impacting the rest of the application.

It's possible to scale monolith software, but it has to be scaled holistically using the vertical scaling approach, which is expensive and inefficient. If you want to upgrade a specific part, there is no escape from rebuilding and redeploying the entire application. So, opt for a monolithic if your solution is not complex and will only be used by a limited number of people.

Microservices architecture

Microservices are more flexible than monoliths. Applications designed in this style consist of many components that work together but are deployed independently. Every component offers a specific functionality. Services constituting one application can have different tech stacks and access different databases. For example, an eCommerce app built as microservices will have one service for product search, another for user profiles, yet another for order handling, and so on.

Microservice application components can be scaled independently without taxing the entire software. So, if you are looking for a scalable solution, microservices are your go-to design. High software scalability is just one of the many advantages you can gain from this architecture. For more information, check out our article on the benefits of microservices.

Tip #8: Monitor performance to determine when to scale

After deployment, you can monitor your software to catch early signs of performance degradation that can be resolved by scaling. This gives you an opportunity to react before the problem escalates. For instance, when you notice that memory is running low or that messages are waiting to be processed longer than the specified limit, this is an indication that your software is running at its capacity.

To be able to identify these and other software scalability-related issues, you need to embed a telemetry monitoring system into your application during the coding phase. This system will enable you to track:

• Average response time
• Throughput, which is the number of requests processed at a given time
• The number of concurrent users
• Database performance metrics, such as query response time
• Resource utilization, such as CPU, memory usage, GPU
• Error rates
• Cost per user

You can benefit from existing monitoring solutions and log aggregation frameworks, such as Splunk. If your software is running in the cloud, you can use the cloud vendor's solution. For example, Amazon offers AWS CloudWatch for this purpose.

Examples of scalable software solutions from ITRex portfolio

Smart fitness mirror with a personal coach

Project description

The client wanted to build a full-length wall fitness mirror that would assist users with their workout routine. It could monitor user form during exercise, count the reps, and more. This system was supposed to include software that allows trainers to create and upload videos, and users to record and manage their workouts.

What we did to ensure the scalability of the software

• We opted for microservices architecture
• Implemented horizontal scalability for load distribution. A new node was added whenever there was too much load on the existing ones. So, whenever CPU usage was exceeding 90% of its capacity and staying there for a specified period of time, a new node would be added to ease the load.
• We had to deploy relational databases – i.e., SQL and PostgreSQL – for architectural reasons. Even though relational databases are harder to scale, there are still several options. In the beginning, as the user base was still relatively small, we opted for vertical scaling. If the audience grew larger, we were planning on deploying the master-slave approach – distributing the data across several databases.
• Extensively benefited from caching as this system contains lots of static information, such as trainers' names, workout titles, etc.
• Used RestAPI for asynchronous request processing between the workout app and the server
• Relied on serverless architecture, such as AWS Lambda, for other types of asynchronous processing. One example is asynchronous video processing. After a trainer loads a new workout video and segments it into different exercises, they press “save,” and the server starts processing this video for HTTP live streaming to construct four versions of the original video with different resolutions. The trainer can upload new videos simultaneously.
• In another example, the system asynchronously performs smart trimming on user videos to remove any parts where the user was inactive.

Biometrics-based cybersecurity system

Project description

The client wanted to build a cybersecurity platform that enables businesses to authenticate employees, contractors, and other users based on biometrics, and steer clear of passwords and PINs. This platform also would contain a live video tool to remotely confirm user identity.

How we ensured this software was scalable

• We used a decentralized microservices architecture
• Deployed three load balancers to distribute the load among different microservices
• Some parts of this platform were autoscalable by design. If the load surpassed a certain threshold, a new instance of a microservice was automatically created
• We used six different databases – four PostgreSQLs and two MongoDBs. The PostgreSQL databases were scaled vertically when needed. While designing the architecture, we realized that some of the databases would have to be scaled rather often, so we adopted MongoDB for that purpose, as they are easier to scale horizontally.
• Deployed asynchronous processing for better user experience. For instance, video post-processing was done asynchronously.
• We opted for a third-party service provider's facial recognition algorithm. So, we made sure to select a solution that was already scalable and incorporated it into our platform through an API.

Challenges you might encounter while scaling

If you intend to plan for software scalability during application development and want to incorporate the tips above, you can still face the following challenges:

• Accumulated technical debt. Project stakeholders might still attempt to sideline scalability in favor of lower costs, speed, etc. Scalability is not a functional requirement and can be overshadowed by more tangible characteristics. As a result, the application will accumulate technical features that will not be compatible with scalability.
• Scaling with Agile development methodology. Agile methodology is all about embracing change. However, when the client wants to implement too many changes too often, software scalability can be put aside for the sake of accommodating changing demands.
• Scalability testing. It's hard to perform realistic load testing. Let's say you want to test how the system will behave if you increase the database size 10 times. You will need to generate a large amount of realistic data, which matches your original data characteristics, and then generate a realistic workload for both writes and reads.
• Scalability of third-party services. Make sure that your third-party service provider doesn't limit scalability. When selecting a tech vendor, verify that they can support the intended level of software scalability, and integrate their solution correctly.
• Understanding your application's usage. You need to have a solid view of how your software will work and how many people will use it, which is rarely possible to estimate precisely.
• Architectural restrictions. Sometimes you are limited in your architectural choices. For example, you might need to use a relational database and will have to deal with scaling it both horizontally and vertically.
• Having the right talent. In order to design a scalable solution that will not give you a headache in the future, you need an experienced architect who worked on similar projects before and who understands software scalability from both coding and infrastructure perspectives. Here at ITRex Group, we've worked on many projects and always keep scalability in mind during software development.

To sum up

Unless you are absolutely positive that you will not need to scale, consider software scalability at early stages of development and take the necessary precautions. Even if you are limited in your architectural choices and can't always implement the most scalable option, you will still know where the obstacles are and will have time to consider alternatives.

Leaving scalability out for the sake of other functional requirements will backfire. First, the company will struggle with performance degradation. It will take too long to process requests. Users will experience unacceptable delays. After all this, the company will scale paying double and triple the amount that could've been spent at earlier stages.

Considering deploying new enterprise software or updating an existing system, but worried it won't keep up with rapidly expanding business needs? Get in touch! We will make sure your software not only has all the required functionality but also can be scaled with minimal investment and downtime.

The post What Is Software Scalability? appeared first on Datafloq.

Recent reports suggest the global cybersecurity market will reach $424.97 billion by 2030, while the data science industry is projected to grow at a rate of 30.1% per year between 2021 and 2028. Thus, both careers promise growth, and thus, choosing the right career can be difficult. 

What is Data Science?

Data science is about working with large amounts of data to obtain meaningful information. It is like being a detective who searches for patterns and solutions among a jumble of clues. You analyze data using math, statistics, and computer abilities to uncover hidden insights. Thereafter, you provide these insights in a form that others can grasp, allowing them to make better decisions. 

Importance of Data Science

• It extracts insights from big data.
• Advanced analytics optimize efficiency and resource usage.
• Data Science enhances decision-making, strategy, and revenue.
• Data Science fuels innovation and business growth in the digital era.

What is Cybersecurity?

Cybersecurity is about safeguarding computers, networks, and data from malicious individuals. It is like having a shield that protects your digital data. Cybersecurity prevents unauthorized access, detecting and responding to cyberattacks using methods and technology. 

Importance of Cybersecurity

• Smoothens remote working
• Streamlines access to the data
• Defends against online threats
• Enhances the trust of consumers in the company

Difference Between Cybersecurity and Data Science

When discussing Cyber Security vs. Data Science, there are many ways in which they are different. Here are those:

1. Focus

Cybersecurity: Cybersecurity aims to protect computers, networks, and data from unauthorized access. It is part of implementing preventative measures, detecting vulnerabilities, and responding to security events.

Data Science: Data science studies data to gain insights and improve decision-making. It refers to various approaches and procedures for collecting, analyzing, and interpreting data to provide actionable insights and help decision-making processes.

2. Objectives

Cybersecurity: The major aim of cybersecurity is to secure the confidentiality, integrity, and availability of information systems and data. Its goal is to keep digital assets safe and restrict unauthorized access. Its goal is also to reduce possible risks and dangers.

Data Science: Data science aims to find patterns, trends, and correlations in data to gain insights and make data-driven predictions or judgments. Its purpose is to extract knowledge from data to solve complex problems, optimize procedures, and foster innovation.

3. Skill Sets

Cybersecurity: Cybersecurity specialists must be familiar with computer networks, information security ideas, encryption technologies, vulnerability assessment methodology, and incident response processes. They must be familiar with network security, encryption, ethical hacking, and security operations.

Data Science: Data scientists must be capable of doing statistical analyses, programming, and machine learning. They must be proficient in statistical analysis, machine learning, data visualization, and programming languages like Python or R. 

4. Applications

Cybersecurity: Cybersecurity protects sensitive data in various industries and enterprises, including financial data, personal records, intellectual property, and government networks. Organizations, governments, and individuals must safeguard their digital assets from cyber threats.

Data Science: Data science is used in various sectors, such as business analytics, healthcare, finance, marketing, and social sciences. It is used to identify trends in customer behavior, improve operational processes, create predictive models, and enhance evidence-based decision-making.

Cyber Security Vs Data Science: Comparing Career Option

There are several significant distinctions when comparing cybersecurity and data science career opportunities. Here is a table comparing these fields:

Cyber Security Vs Data Science: Which One is Better?

Cyber Security and Data Science offer promising career options with unique opportunities and challenges. The decision between the two is based on your interests, talents, and future goals. 

Consider your educational background, skill set, and industry of interest. Investigate the employment market and prospective career advancement options. Seek advice from specialists in each discipline that shall help you make the right decision.

The post Cyber Security or Data Science: Pick the Best Career Option appeared first on Datafloq.

Business data processing can be carried out manually, however, it is generally automated using programming languages, software tools, or complex data processing platforms. Automation helps boost efficiency, decreases errors, and enables the processing of data at large-scale.

The common steps involved in data processing include the following:

Data Collection

This step includes collecting data from multiple sources, such as sensors, external data providers, and databases. The gathered data can be organized in a predetermined way, or it can be unstructured, lacking any particular format, such as social media posts or text documents.

Data Validation and Cleaning

The collected data is examined in this step for any mistakes, discrepancies, or missing values. Data validation guarantees that the data is correct, comprehensive, and complies with established guidelines or criteria for quality. Data cleaning involves fixing errors, handling missing data, eliminating duplicates, and standardizing data formats.

Data Transformation

After the data has been cleaned, it frequently has to be converted into a format that is better suited for analysis or integration. Data normalization, summarization, aggregation, or the combining of several datasets can all be components of this process.

Data Visualization and Reporting

After analysis, charts, graphs, dashboards, or other visual representations are used to visualize the processed data. Complex information is easier to access and understand when it is visualized. To help stakeholders make data-driven decisions, reports, and summaries are produced to present the findings.

Data Storage and Integration

Processed data is generally stored in data warehouses, databases, or data lakes for later usage. Integrating data from several sources results in a unified view that allows for thorough analysis and reporting across numerous datasets.

In today's data-driven economy, data processing services have a substantial impact on corporate growth and scalability. To extract useful insights and encourage educated decision-making, these services entail the gathering, analysis, transformation, and interpretation of massive volumes of data.

Here are some ways that data processing services aid in the scalability and expansion of businesses:

Better Productivity and Efficiency

With the help of effective data processing services, businesses can automate time-consuming and repetitive operations thanks to. Data processing services can streamline procedures, remove manual errors, and quicken data analysis by utilizing technology like machine learning and artificial intelligence. Owing to this improved efficiency, businesses can concentrate on their core functions, boost production, and scale operations successfully.

Improved Decision-Making

By transforming unstructured data into useful information, data processing services assist businesses make data-driven decisions. Businesses can learn a lot about customer behavior, market trends, operational efficiency, and other important factors by analyzing and understanding massive datasets. These insights allow organizations to find new growth prospects, streamline operations, and make well-informed decisions.

Infrastructure Scalability

Cloud-based technologies are frequently used by business data processing services, enabling organizations to extend their data processing infrastructure in response to demand. Scalable resources like storage and processing power are available through cloud platforms, and they may be easily adjusted to meet growing data volumes and processing needs. Because of this scalability, organizations are no longer needed to make large upfront infrastructure investments and can respond swiftly to evolving customer demands.

Real-time Data Insights

Data processing assists businesses in analyzing data in real time and offering the most recent updates for timely decision-making. Organizations can monitor important performance metrics, spot abnormalities or new trends, and act quickly with the help of real-time data processing. This responsiveness and flexibility help businesses in seizing opportunities, obtain a competitive edge in dynamic markets and address issues in a proactive manner.

More Customer Satisfaction

Through data analysis, data processing services assist organizations in understanding customer preferences, behavior, and demands. Businesses can personalize their marketing campaigns, product recommendations, and overall customer experience by utilizing customer data. This tailored strategy boosts client satisfaction, fosters loyalty, and stimulates revenue growth.

Cost Savings

With the help of data processing outsourcing, businesses get access to a cost-effective solution for their processing needs. Organizations can benefit from the experience of data processing service providers rather than investing in expensive infrastructure, software, and qualified staff. These providers offer flexible pricing structures that let companies pay for the services they require, lowering up-front expenditures and improving cost effectiveness.

Advanced Analytics Capabilities

Data processing services offer advanced analytics capabilities that can easily scale with increasing data volumes. These services are capable of handling big datasets and carrying out complex analysis including data mining, predictive modeling, and trend analysis. Utilizing scalable data analytics, companies can find insightful information that encourages innovation, improves operations, and promotes business growth.

Conclusion

Data processing services equip businesses with the scalability, cost-effectiveness, and expertise needed to handle huge volumes of data in an efficient manner. By outsourcing these services, businesses can concentrate on their core capabilities while utilizing specialized tools and resources to handle data efficiently and generate insightful information.

In general, data processing is a vital step in obtaining value from raw data, allowing organizations and businesses to gain insights, improve decision-making, streamline processes, and promote business growth. Data processing services are essential for business scalability and growth. Businesses are able to make informed decisions, improve operational efficiency, enhance customer experiences, and unleash new growth prospects by utilizing the power of data analysis, automation, scalability, and real-time insights.

The post Impact of Data Processing Services on Scalability and Business Growth appeared first on Datafloq.

Regression models are an essential component of machine learning, enabling computers to make predictions and understand patterns in data without explicit programming. Sklearn, a powerful machine learning library, offers a range of regression models to facilitate this process.

Before delving into the specific regression methods in Sklearn, let's briefly explore the three types of machine learning models that can be implemented using Sklearn Regression Models:

• reinforced learning,
• unsupervised learning
• supervised learning

These models allow computers to learn from data, make decisions, and perform tasks autonomously. Now, let's take a closer look at some of the most popular regression methods available in Sklearn for implementing these models.

Linear Regression

Linear regression is a statistical modeling technique that aims to establish a linear relationship between a dependent variable and one or more independent variables. It assumes that there is a linear association between the independent variables and the dependent variable, and that the residuals (the differences between the actual and predicted values) are normally distributed.

Working principle of linear regression

The working principle of linear regression involves fitting a line to the data points that minimizes the sum of squared residuals. This line represents the best linear approximation of the relationship between the independent and dependent variables. The coefficients (slope and intercept) of the line are estimated using the least squares method.

Implementation of linear regression using sklearn

Sklearn provides a convenient implementation of linear regression through its LinearRegression class. Here's an example of how to use it:

from sklearn.linear_model import LinearRegression
# Create an instance of the LinearRegression model

model = LinearRegression()
# Fit the model to the training data

model.fit(X_train, y_train)
# Predict the target variable for new data

y_pred = model.predict(X_test)

Polynomial Regression

Polynomial regression is an extension of linear regression that allows for capturing nonlinear relationships between variables by adding polynomial terms. It involves fitting a polynomial function to the data points, enabling more flexible modeling of complex relationships between the independent and dependent variables.

Advantages and limitations of polynomial regression

The key advantage of polynomial regression is its ability to capture nonlinear patterns in the data, providing a better fit than linear regression in such cases. However, it can be prone to overfitting, especially with high-degree polynomials. Additionally, interpreting the coefficients of polynomial regression models can be challenging.

Applying polynomial regression with sklearn

Sklearn makes it straightforward to implement polynomial regression. Here's an example:

from sklearn.preprocessing import PolynomialFeatures

from sklearn.linear_model import LinearRegression

from sklearn.pipeline import make_pipeline

# Create polynomial features

poly_features = PolynomialFeatures(degree=2)

X_poly = poly_features.fit_transform(X)
# Create a pipeline with polynomial regression

model = make_pipeline(poly_features, LinearRegression())
# Fit the model to the training data

model.fit(X_train, y_train)
# Predict the target variable for new data

y_pred = model.predict(X_test)

In the code snippet above, X represents the independent variable values, X_poly contains the polynomial features created using PolynomialFeatures, and y represents the corresponding target variable values. The pipeline combines the polynomial features and the linear regression model for seamless implementation.

Evaluating polynomial regression models

Evaluation of polynomial regression models can be done using similar metrics as in linear regression, such as MSE, R score, and RMSE. Additionally, visual inspection of the model's fit to the data and residual analysis can provide insights into its performance.

Polynomial regression is a powerful tool for capturing complex relationships, but it requires careful tuning to avoid overfitting. By leveraging Sklearn's functionality, implementing polynomial regression models and evaluating their performance becomes more accessible and efficient.

Ridge Regression

Ridge regression is a regularized linear regression technique that introduces a penalty term to the loss function, aiming to reduce the impact of multicollinearity among independent variables. It shrinks the regression coefficients, providing more stable and reliable estimates.

The motivation behind ridge regression is to mitigate the issues caused by multicollinearity, where independent variables are highly correlated. By adding a penalty term, ridge regression helps prevent overfitting and improves the model's generalization ability.

Implementing ridge regression using sklearn

Sklearn provides a simple way to implement ridge regression. Here's an example:

from sklearn.linear_model import Ridge
# Create an instance of the Ridge regression model

model = Ridge(alpha=0.5)
# Fit the model to the training data

model.fit(X_train, y_train)
# Predict the target variable for new data

y_pred = model.predict(X_test)

In the code snippet above, X_train represents the training data with independent variables, y_train represents the corresponding target variable values, and X_test is the new data for which we want to predict the target variable (y_pred). The alpha parameter controls the strength of the regularization.

To assess the performance of ridge regression models, similar evaluation metrics as in linear regression can be used, such as MSE, R score, and RMSE. Additionally, cross-validation and visualization of the coefficients' magnitude can provide insights into the model's performance and the impact of regularization.

Lasso Regression

Lasso regression is a linear regression technique that incorporates L1 regularization, promoting sparsity in the model by shrinking coefficients towards zero. It can be useful for feature selection and handling multicollinearity.

Lasso regression can effectively handle datasets with a large number of features and automatically select relevant variables. However, it tends to select only one variable from a group of highly correlated features, which can be a limitation.

Utilizing lasso regression in sklearn

Sklearn provides a convenient implementation of lasso regression. Here's an example:

from sklearn.linear_model import Lasso
# Create an instance of the Lasso regression model

model = Lasso(alpha=0.5)
# Fit the model to the training data

model.fit(X_train, y_train)
# Predict the target variable for new data

y_pred = model.predict(X_test)
In the code snippet above, X_train represents the training data with independent variables, y_train represents the corresponding target variable values, and X_test is the new data for which we want to predict the target variable (y_pred). The alpha parameter controls the strength of the regularization.

Evaluating lasso regression models

Evaluation of lasso regression models can be done using similar metrics as in linear regression, such as MSE, R score, and RMSE. Additionally, analyzing the coefficients' magnitude and sparsity pattern can provide insights into feature selection and the impact of regularization.

Support Vector Regression (SVR)

Support Vector Regression (SVR) is a regression technique that utilizes the principles of Support Vector Machines. It aims to find a hyperplane that best fits the data while allowing a tolerance margin for errors.

SVR employs kernel functions to transform the input variables into higher-dimensional feature space, enabling the modeling of complex relationships. Popular kernel functions include linear, polynomial, radial basis function (RBF), and sigmoid.

Implementing SVR with sklearn

Sklearn offers an implementation of SVR. Here's an example:

from sklearn.svm import SVR

# Create an instance of the SVR model

model = SVR(kernel='rbf', C=1.0, epsilon=0.1)
# Fit the model to the training data

model.fit(X_train, y_train)
# Predict the target variable for new data

y_pred = model.predict(X_test)

In the code snippet above, X_train represents the training data with independent variables, y_train represents the corresponding target variable values, and X_test is the new data for which we want to predict the target variable (y_pred). The kernel parameter specifies the kernel function, C controls the regularization, and epsilon sets the tolerance for errors.

Evaluating SVR models

SVR models can be evaluated using standard regression metrics like MSE, R score, and RMSE. It's also helpful to analyze the residuals and visually inspect the model's fit to the data for assessing its performance and capturing any patterns or anomalies.

Decision Tree Regression

Decision tree regression is a non-parametric supervised learning algorithm that builds a tree-like model to make predictions. It partitions the feature space into segments and assigns a constant value to each region. For a more detailed introduction and examples, you can click here: decision tree introduction.

Applying decision tree regression using sklearn

Sklearn provides an implementation of decision tree regression through the DecisionTreeRegressor class. It allows customization of parameters such as maximum tree depth, minimum sample split, and the choice of splitting criterion.

Evaluation of decision tree regression models involves using metrics like MSE, R score, and RMSE. Additionally, visualizing the decision tree structure and analyzing feature importance can provide insights into the model's behavior.

Random Forest Regression

Random forest regression is an ensemble learning method that combines multiple decision trees to make predictions. It reduces overfitting and improves prediction accuracy by aggregating the predictions of individual trees.

Random forest regression offers robustness, handles high-dimensional data, and provides feature importance analysis. However, it can be computationally expensive and less interpretable compared to single decision trees.

Implementing random forest regression with sklearn

Sklearn provides an easy way to implement random forest regression. Here's an example:

from sklearn.ensemble import RandomForestRegressor
# Create an instance of the Random Forest regression model

model = RandomForestRegressor(n_estimators=100)
# Fit the model to the training data

model.fit(X_train, y_train)
# Predict the target variable for new data

y_pred = model.predict(X_test)

In the code snippet above, X_train represents the training data with independent variables, y_train represents the corresponding target variable values, and X_test is the new data for which we want to predict the target variable (y_pred). The n_estimators parameter specifies the number of trees in the random forest.

Evaluating random forest regression models

Evaluation of random forest regression models involves using metrics like MSE, R score, and RMSE. Additionally, analyzing feature importance and comparing with other regression models can provide insights into the model's performance and robustness.

Gradient Boosting Regression

Gradient boosting regression is an ensemble learning technique that combines multiple weak prediction models, typically decision trees, to create a strong predictive model. It iteratively improves predictions by minimizing the errors of previous iterations.

Gradient boosting regression offers high predictive accuracy, handles different types of data, and captures complex interactions. However, it can be computationally intensive and prone to overfitting if not properly tuned.

Utilizing gradient boosting regression in sklearn

Sklearn provides an implementation of gradient boosting regression through the GradientBoostingRegressor class. It allows customization of parameters such as the number of boosting stages, learning rate, and maximum tree depth.

Evaluating gradient boosting regression models

Evaluation of gradient boosting regression models involves using metrics like MSE, R score, and RMSE. Additionally, analyzing feature importance and tuning hyperparameters can optimize model performance. For a more detailed introduction and examples, you can click here: gradient boosting decision trees in Python.

Conclusion

In conclusion, we explored various regression models and discussed the importance of choosing the appropriate model for accurate predictions. Sklearn's regression models offer a powerful and flexible toolkit for predictive analysis, enabling data scientists to make informed decisions based on data.

The post Mastering Regression Analysis with Sklearn: Unleashing the Power of Sklearn Regression Models appeared first on Datafloq.

Laravel's Popularity Surge

Laravel was first introduced in 2011, and its popularity has skyrocketed since then. The framework has attracted a strong and dedicated community of developers due to its simplicity, flexibility, and extensive documentation. Let's take a closer look at some compelling Laravel usage statistics that highlight its widespread adoption.

GitHub Stars: A strong indicator of a project's popularity and community support, Laravel has amassed over 67,000 stars on GitHub as of the time of writing. This impressive number indicates the high level of interest and engagement from developers around the world.

Downloads: Laravel has witnessed exponential growth in terms of downloads. According to recent statistics, the framework has been downloaded over 180 million times from the official package repository, Packagist. This staggering number demonstrates the trust and reliance placed on Laravel for web development projects.

Job Market Demand: The demand for Laravel developers has been steadily increasing. Many organizations are seeking skilled Laravel developers to build and maintain their web applications. A quick search on popular job portals reveals a multitude of job openings for Laravel developers. This high demand is a testament to Laravel's prominence in the industry.

Framework Performance: Laravel has consistently outperformed other PHP frameworks in terms of performance benchmarks. With each new release, the framework undergoes optimizations and enhancements that make it faster and more efficient. Developers can leverage Laravel's performance benefits to build high-performing web applications.

Laravel Ecosystem: Laravel's success can be attributed, in part, to its robust ecosystem. The framework offers a wide array of official and community-driven packages that extend its functionality. These packages enable developers to build complex features and functionalities more efficiently. The Laravel ecosystem also includes various tools, libraries, and integrations, providing developers with the resources they need to streamline their development process.

The Future of Web Development

Based on the Laravel usage statistics and the current trends in the web development landscape, several key insights emerge about the future of web development.

Increased Laravel Adoption: As Laravel continues to gain momentum, its adoption rate is likely to surge even further. Its ease of use, elegant syntax, and comprehensive documentation make it an attractive choice for developers, especially those who are new to PHP. The framework's versatility and ability to handle complex projects position it as a strong contender for web development in the coming years.

Emphasis on Developer Productivity: Laravel's focus on developer productivity has been a driving force behind its popularity. The framework's expressive syntax, powerful features, and extensive libraries enable developers to build applications rapidly. As web development projects become more complex, frameworks like Laravel that prioritize developer productivity will continue to dominate the industry.

Growing Laravel Ecosystem: The Laravel ecosystem will continue to expand as developers contribute more packages, tools, and integrations. This growth will empower developers to leverage pre-built components and streamline their development workflow further. The ecosystem will continue to mature, making Laravel an even more compelling choice for web development projects.

Shift Towards API-Driven Development: With the rise of mobile applications, the demand for API-driven development has increased. Laravel's built-in support for building robust APIs positions it as a leading framework in this domain. As web applications continue to evolve, Laravel's API capabilities will play a crucial role in meeting the demand for seamless integrations and data exchange between systems.

Conclusion

The Laravel usage statistics paint a promising picture for the future of web development. The framework's popularity, community support, and strong ecosystem demonstrate its effectiveness and potential. As more businesses recognize the value of Laravel, the demand for skilled Laravel developers will continue to rise. Emphasizing developer productivity and providing an extensive set of features, Laravel is poised to play a significant role in shaping the web development landscape in the coming years.

If you're planning a web development project, considering the hire of Laravel developers is a wise choice. Their expertise in leveraging Laravel's capabilities can help you build robust, scalable, and high-performing web applications that meet your business objectives. With the increasing adoption of Laravel, you can be confident that you're investing in a framework that aligns with the future of web development.

The post Laravel Usage Statistics: What They Tell Us About the Future of Web Development appeared first on Datafloq.

Here's what he has to say about it.

As a QA Team Lead at an enterprise software development company ITRex, I'm perfectly aware of our client's aspirations to reduce software development costs while launching a fully functioning product on time and to maximum value.

While these goals are understandable, I advise against dismissing your QA team early in the project, even if they don't find bugs on a daily basis, although this might seem an easy option to cut the paycheck and speed up software release cycles.

Also, I recommend you follow quality assurance best practices throughout the project to validate that your solution and all of its features function as expected and do not compromise your cybersecurity.

And one of such practices is creating and maintaining proper QA documentation.

What is quality assurance documentation exactly? How can it help you reap the most benefit from tapping into QA and testing services? And is there a way to optimize the costs and effort associated with preparing QA documentation while minimizing the risk of developing a poorly architected, bug-ridden application and having to rebuild the whole thing from the ground up?

Let's find that out!

Introduction to QA documentation

QA documentation is a collection of documents and artifacts created and maintained by a quality assurance team during the software development and testing process.

It may include various documents that outline the testing strategy, test plans, test cases, test scripts, test data, test logs, bug reports, and any other documentation related to the QA activities. These documents facilitate communication among QA team members, provide guidelines for testing, and help in identifying and resolving issues efficiently.

Thus, QA documentation plays a vital role in ensuring the quality and reliability of software products – and that's the major objective our clients pursue.

What QA documents are used in software projects

For this article's purpose, we'll give you a brief overview of quality assurance documents that form the backbone of testing documentation in a software development project:

• A test plan is a QA document that outlines the overall approach, goals, scope, resources, and schedule of software testing activities. Simply put, it covers:

1. The name and description of a project, including the types of apps under testing and their core functionality
2. The preferred testing methods (manual, automated, mixed) and test types (new features, integrations, compatibility, regression, etc.)
3. The features that need to be tested, alongside an approximate schedule for each testing activity
4. Optimum team composition
5. An overview of risks and issues that might arise during the testing process
6. A list of testing documents that your QA team will use during the project

A rule of thumb is to write a test plan at the beginning of a software project when your IT team defines functional and non-functional requirements for a software solution, chooses an appropriate technology stack and project management methodology, and creates a project roadmap.

It normally takes up to three days to put up and review a simple test plan without test cases.

• Test cases describe specific test scenarios, including the input data, expected results, and steps to execute. Test cases are designed to verify the functionality, performance, or other aspects of a software product. Please note that test cases are used by both manual testing services and QA automation services teams. This way, you'll ensure maximum test coverage, meaning no bugs will manifest themselves in production code.

Even though a skilled QA engineer could write a high-level test case in just ten minutes, the number of test cases for a medium-sized project could easily exceed 4,000 (and counting). Multiply that number by the average middle QA engineer hourly rate ($65 per man hour for the North American market), and you'll arrive at an impressive figure.

• Checklists are concise, itemized lists of actions or tasks that need to be completed or verified during the testing process. Thus, a checklist in QA documentation usually includes a complete rundown of functional modules, sections, pages, and other elements of an app or cyber-physical system that require a QA team's attention.

In smaller projects, checklists can successfully replace detailed test cases (more on that later.)

• Test scripts are chunks of code written using specific testing tools or frameworks, such as Selenium, Appium, and Cucumber. These scripts automate the execution of test cases, making the testing process more efficient – specifically, in large and complex software projects like multi-tenant SaaS systems and popular B2C apps, which are updated frequently and where even the smallest bugs may negatively impact user experience.
• Test data is the data used by QA engineers to assess the performance, functionality, reliability, and security of a software solution under various conditions. It may include sample input values, boundary conditions, and various scenarios. For instance, your QA team may use positive and negative test data to validate that only correct login credentials may be used for entering a software system. Similarly, test data can be used for implementing age restrictions in certain types of apps or investigating how an application handles increased workloads.

• Test logs document the test execution process, including the date and time of test performance, the summary of the executed test cases, the results your QA team achieved, screenshots, and any issues or observations noted during testing. A test log is a vital source of information for tracking the testing progress, identifying patterns or trends in test results, and providing a historical record of the testing activities. It helps identify and resolve issues efficiently and serves as a reference for future testing efforts or audits.
• Defect or bug reports are testing documents that detail defects and issues found during QA activities. Specifically, they describe the detected bugs, their severity and priority, and the conditions under which the defects occur. A QA manager uses bug reports to assign tasks to software testing specialists and track their status.

• A traceability matrix maps the relationship between test cases and requirements or other artifacts. It helps ensure that all requirements are adequately covered by test cases, allows for tracking the test coverage across the project, and eliminates redundant testing activities.
• A test completion report summarizes the testing activities performed in a project, including the test execution status, the number of test cases executed, defects found, and any pending tasks.

Why is QA documentation important?

Having quality assurance documentation helps attain the exact results that the customer and the software engineering team expect.

This is achieved by a combination of factors, including the following:

1. QA documentation provides clear instructions and guidelines that software testing specialists can follow to perform tasks consistently, reducing variations and improving the overall quality of products or services.
2. Quality assurance documentation reduces the likelihood of detecting critical defects and errors in software solutions late in the development process, thus playing a pivotal role in budget control. QA experts suggest that the cost of fixing bugs increases exponentially with every project stage, ranging from 3X for the design/architecture phase to 30X and more for the deployment phase.
3. Quality assurance documentation helps ensure compliance with the regulatory requirements and standards your organization must meet by simplifying audits and providing evidence of established processes, procedures, and quality controls.
4. By documenting procedures, controls, and risk assessment processes, software testing documentation helps organizations identify potential risks and take preventive measures to minimize their impact on their business and customer satisfaction.
5. New hires can refer to your QA documentation to understand the quality processes and procedures in a software project, reducing the learning curve and ensuring consistent training across the organization.
6. By documenting non-conformances, corrective actions, and lessons learned, companies can identify areas for improvement and implement changes to enhance efficiency and quality.
7. Having well-documented QA processes and procedures can enhance customer confidence in your company's products or services. Extensive software testing documentation demonstrates a commitment to quality and assures that the organization has robust systems in place to deliver consistent and reliable results.
8. In situations where legal disputes or product recalls arise, QA documentation can serve as important evidence. It can demonstrate that your organization has followed established quality processes, taken necessary precautions, and fulfilled its obligations.

How long does it take to create QA documentation?

An honest answer to this question will be, “It depends.”

Specifically, the timeframe and the associated costs depend on several factors, such as the size of your organization and the complexity of its processes, the industry you're in, and the type of software you're building.

If you've previously embarked on software development projects and have an in-house QA team, you might be able to reuse existing QA documentation for new projects. Using templates and specialized tools for creating and maintaining software testing documentation, such as project management and wiki software, is helpful, too.

Do you always need QA documentation – and is it possible to reduce its creation and maintenance costs?

However useful, quality assurance documentation may increase software project costs due to the additional effort and personnel required for its creation and maintenance.

This might be an issue for startups operating on a shoestring or enterprises undergoing digital transformation in times of recession.

Does every type of software project need super-detailed QA documentation then – and is it possible to reduce the costs associated with it?

To determine the best approach to QA document creation, consider the following factors:

• Project size and budget. In the case of small-budget and short-term projects (unless we talk about highly innovative and technical projects executed by large IT teams), there is no need to overcomplicate the documentation process, so your QA squad can opt for checklists instead of detailed test cases. Regarding the test plan document, which determines the overall testing strategy, we can also forgo writing it in cases where there is no budget for it or if the project is short-term and does not involve leading-edge technologies.
• QA team size and experience. The more QA engineers on the project and the less experience they have in quality assurance, the more challenging it is to control the testing process. Therefore, you need extensive quality assurance documentation to keep the team members on the same page. In such cases, it is advisable to lean towards test cases rather than checklists to more effectively distribute tasks among engineers based on their experience and knowledge, and to involve more experienced QA specialists, who normally have higher hourly rates, in test case creation.
• Agile vs. Waterfall approach to project management. While the ITRex team has summarized the key differences between Agile and Waterfall methodologies in this blog post, it's worth mentioning what sets the two approaches apart in terms of quality assurance. In Waterfall, software testing is saved for last, meaning your QA team will conduct tests only when the coding part is 100% complete. For obvious reasons, they can't do it without proper quality assurance documentation, which should be prepared during the requirements elicitation phase. In Agile, where IT teams tend to build smaller pieces of software iteratively and test the code at the end of each cycle, creative comprehensive QA documentation beforehand is not preferred. Still, I recommend you write a test plan to better align the current situation with the customer's and software engineers‘ expectations.

Overall, having QA documentation could benefit any software development project, no matter the complexity and size.

As a client-oriented company, however, we're always ready to suggest workarounds considering your objectives and budget.

If you aren't sure whether you need to prepare extensive quality assurance documentation for your project and looking for skilled QA engineers to entrust the task to, contact ITRex! We'll make sure you launch a high-performance, bug-free software solution on time, on budget, and up to spec!

The post QA Documentation: What Is It & Do You Always Need It? appeared first on Datafloq.

Data Annotation at Glance

Data annotation in Machine Learning is the process of tagging data available in various formats such as text, video, images, and audio. These labels help the Machine Learning algorithms to comprehend the data and perform the desired actions through supervised learning. This way, machines can understand the input patterns, detect and identify objects, and calculate attributes with ease.

To get a better understanding of this, here are different types of data annotation:

Text Annotation

Text annotation is the most generally used data category. If you want AI/ML-based models to understand what is written, you need to use text, creating the need for text annotation. In this, annotators label and provide metadata for your textual data. Simply put, professional labelers annotate the text to tell the machine what the text is saying. These labels can be used to add information about the text's structure, meaning, and sentiment, among other things.

Image Annotation

Image annotation plays a key role in powering Computer Vision-based models. Here, annotations are added to different items inside an image, which could be in the form of tags, captions, identifiers, meta descriptions, or keywords. Annotated image data also makes it easy for robots to understand and interpret the visual information they are fed with. This is essential for improving robotic vision, biometric identification, facial recognition, and security solutions.

Video Annotation

As the name suggests, items in the video are labeled with relevant tags and descriptions so that machines can comprehend what's in the video. The video annotation process helps in improving video monitoring and security applications. Unlike image annotation, video annotation is comparatively difficult as the object of interest is constantly moving in the script.

One of the prominent examples of video annotation in real life is autonomous vehicles or self-driven cars. Large amounts of video data are gathered and annotated with detailed information such as the location of traffic lights, directions, stop signs, and other vehicles to train a self-driving car to navigate roads and avoid obstacles. The car's Machine Learning algorithms leverage this annotated data to recognize and respond to different objects and situations that come its way in real-time.

Audio Annotation

Here, speech data is transcribed and time-stamped. Audio annotation includes the transcription of speech as well as pronunciation along with the identification of language, dialect, and speaker demographics. This can be used as an excellent security application. For instance, security gadgets can notify the authorities by identifying the sound of glass breaking.

Advantages and Limitations of Outsourcing Data Annotation Process

The success of any AI/ML model is directly related to the quality of annotated datasets used to train them. Any errors or trials in the initial stages can toss up the entire thing, resulting in lost time, effort, and money. Hence, you've got to consider all the aspects to make an informed decision while outsourcing data annotation services in Machine Learning.

Pros of Outsourced Data Annotation

When you outsource data annotation services, you get professional excellence and technological competence as non-negotiable benefits. In addition, you can reap other advantages as listed here:

Experiential Expertise

Data annotation companies have a pool of skilled annotators and data professionals who specialize in various annotation tasks. These specialists are trained in specific annotation techniques and possess domain knowledge. They combine skill, experience, and expertise to ensure excellence in every annotation endeavor.

Scalability and Speed

One of the tangible advantages of outsourcing vital tasks like data labeling is that the professional providers have the right resources and infrastructure to handle large-scale annotation projects efficiently. They can alter their operational approach and quickly scale up or down based on project requirements, allowing for faster turnaround times and increased productivity.

Quality Assurance

Dedicated data annotation service providers have inbuilt robust quality assurance processes in place. They perform checks and audits regularly to ensure consistency, accuracy, and adherence to annotation guidelines and industry standards. This helps maintain quality in the annotation process and minimizes the need for extensive rework.

Versatility

Professional data annotation companies are equipped to handle a wide range of annotation tasks across different data types including images, audio, videos, and text. They can provide custom annotation solutions, adapt to specific project requirements, as well as integrate with existing workflows or Machine Learning frameworks.

Cost Friendly

Outsourcing data annotation to specialized vendors can be cost-effective in various cases as you need not invest in infrastructure, resources, or technologies. Instead of setting-up in-house annotation teams and infrastructure, companies can leverage the expertise of service providers, reducing overhead costs and operational expenses significantly.

Cons of Data Annotation

Just as every coin has two sides, there are certain drawbacks of outsourcing data annotation services as listed here:

Data Privacy and Security Concerns

When you outsource data annotation tasks, you share potentially sensitive or proprietary data with a third-party provider which might be risky for data integrity and security. Therefore, businesses need to carefully evaluate the data privacy and security measures implemented by the service provider to protect the confidentiality of their data.

Communication and Collaboration Challenges

Engaging with a third-party data annotation service provider requires effective communication and collaboration to ensure a proper understanding of project requirements and guidelines. Lack of coordination or miscommunication can result in errors or delays in the annotation process.

Reduced Control and Flexibility

Outsourcing data annotation means giving up some level of control or sharing access over the annotation process. Organizations may have less direct influence over the annotation decisions and might need to rely on the service provider's judgment and expertise.

Final Words

Data annotation is important to fuel AI/ML organizations and expand business paradigms. At the same time, it is equally important for companies to carefully assess their specific requirements, evaluate their capabilities along with their reputation, and consider the trade-offs before finalizing a service provider.

The post The Pros and Cons of Outsourcing Data Annotation Process for Machine Learning appeared first on Datafloq.