Month: February 2024

Why Product Engineering Is Critical for Digital Transformation in Enterprises

Posted on by Indium

Digital transformation has become a strategic priority for enterprises across all sectors. In a world shaped by rapid technological advancements, evolving customer expectations, and the constant need for innovation, enterprises can no longer rely on traditional IT systems and legacy development approaches. They need scalable, agile, and data-driven solutions that align with modern business goals. 

At the heart of this evolution is product engineering—a discipline that encompasses the design, development, deployment, and lifecycle management of digital products. It provides the foundational capabilities that enable businesses to reimagine how they deliver value in a digital-first economy. 

Software product engineering services play a critical role in helping enterprises accelerate innovation, reduce time-to-market, and enhance customer experiences. This blog explores how product engineering supports digital transformation initiatives and why it should be a top priority for enterprise technology leaders. 

What Is Product Engineering? 

Unlike traditional software development, product engineering emphasizes innovation, adaptability, and customer-centricity—key drivers of successful digital transformation. This distinction is often discussed in the broader context of product engineering vs software engineering, where the former takes a lifecycle-centric, business-aligned approach, and the latter emphasizes technical execution and code quality.The key phases typically include: 

  • Product ideation and strategy 
  • Technology architecture and system design 
  • User experience (UX) and user interface (UI) design 
  • Agile software development and coding 
  • Testing and quality assurance 
  • Deployment, maintenance, and continuous improvement 

Unlike traditional software development, product engineering emphasizes innovation, adaptability, and customer-centricity—key drivers of successful digital transformation. 

The Role of Product Engineering in Digital Transformation 

Digital transformation is more than just technology adoption. It is a complete overhaul of how enterprises operate, interact with customers, and deliver value. Product engineering enables this shift by offering a structured yet flexible approach to build future-ready solutions. 

1. Accelerating Innovation 

To remain competitive, enterprises must continuously innovate. Product engineering facilitates the development of forward-thinking solutions that integrate emerging technologies and respond to evolving business needs. It enables organizations to experiment, iterate, and launch new digital capabilities with agility and precision. 

2. Improving Agility and Responsiveness 

Market dynamics are changing faster than ever. Enterprises need to pivot quickly based on customer feedback, market trends, or operational disruptions. Product engineering promotes agile development methodologies that allow teams to adapt, iterate, and deploy updates rapidly—ensuring responsiveness and resilience. 

3. Driving Operational Efficiency 

Through automation, DevOps integration, and streamlined workflows, product engineering reduces manual effort and eliminates redundancies. It enables organizations to optimize their software development lifecycle, reduce technical debt, and ensure higher productivity across teams. 

4. Ensuring Scalability and Reliability 

Enterprise solutions must be built to scale—both in terms of user load and functionality. Product engineering leverages modular architectures, cloud-native development, and robust testing practices to ensure products remain reliable, performant, and secure as they evolve over time. 

5. Enhancing Customer Experience 

Digital transformation is ultimately about delivering superior customer value. Product engineering focuses on usability, accessibility, and experience design, ensuring that software products are intuitive, engaging, and aligned with user expectations. 

Strategic Value of Software Product Engineering Services 

For large enterprises undergoing digital transformation, building and maintaining a skilled internal product engineering team can be complex and resource-intensive. This is where partnering with specialized external product engineering experts becomes valuable. 

These services offer end-to-end support across the entire product lifecycle, helping enterprises: 

  • Access top-tier engineering talent across multiple domains 
  • Leverage proven methodologies and technology stacks 
  • Accelerate go-to-market timelines with scalable delivery models 
  • Ensure continuous delivery, security, and performance optimization 
  • Reduce operational overhead and focus on strategic growth initiatives 

Partnering with a reliable product engineering provider enables enterprises to maintain strategic focus while leveraging expert capabilities to drive transformation. 

Key Capabilities Driving Transformation Through Product Engineering 

To truly align with digital transformation objectives, modern product engineering must encompass several advanced capabilities: 

Cloud-Native Development 

Building applications on cloud platforms enables flexibility, cost-efficiency, and seamless scalability. Product engineering leverages cloud-native services to develop solutions that are resilient, fault-tolerant, and easily deployable across hybrid or multi-cloud environments. 

Agile and DevOps Integration 

Combining Agile methodologies with DevOps practices fosters collaboration, automation, and faster release cycles. Product engineering integrates continuous integration (CI), continuous delivery (CD), and automated testing pipelines for enhanced efficiency and reliability. 

Microservices and API-First Architecture 

Modern software products benefit from microservices and API-driven designs. These architectures allow for modularity, reusability, and easier integration with third-party systems—key for building scalable enterprise ecosystems. 

AI and Data-Driven Intelligence 

Embedding data analytics, machine learning, and AI models into software products enhances decision-making and operational intelligence. Product engineering frameworks now increasingly support real-time data processing, predictive analytics, and intelligent automation. 

Security by Design 

In an era of increasing cyber threats and regulatory scrutiny, product engineering ensures that security is embedded throughout the development lifecycle—from design and code reviews to deployment and maintenance. 

Product Engineering as a Long-Term Business Enabler 

Looking ahead, product engineering is not just a technology function—it’s a strategic enabler of long-term business transformation. It allows enterprises to: 

  • Expand into new markets through faster product localization 
  • Launch new digital products and services with lower risk 
  • Create unified, omnichannel customer experiences 
  • Leverage real-time data to optimize business performance 
  • Respond proactively to market disruptions and emerging trends 

As enterprises continue to embrace cloud, AI, automation, and real-time analytics, product engineering will remain at the forefront of transformation initiatives. 

Frequently Asked Questions (FAQs) 

1. What is the difference between traditional software development and product engineering? 

Traditional software development often focuses on delivering projects with fixed requirements. Product engineering, on the other hand, focuses on long-term product evolution, incorporating continuous improvements, customer feedback, and strategic business alignment. 

2. Why is product engineering important for digital transformation? 

Product engineering enables the creation of modern digital solutions that support enterprise agility, innovation, and scalability—key pillars of digital transformation. 

3. What are software product engineering services? 

Software product engineering services include the strategic design, development, testing, deployment, and support of digital products. They are offered by specialized providers who help enterprises accelerate product development and modernization initiatives. 

4. How does product engineering support enterprise agility? 

Product engineering adopts Agile and DevOps practices, enabling teams to deliver iterative updates, respond quickly to market changes, and maintain flexibility throughout the product lifecycle. 

5. Can enterprises outsource product engineering? 

Yes, outsourcing product engineering to expert service providers gives enterprises access to scalable talent, advanced technology, and faster time-to-market, all while reducing internal overhead. 

6. What role does cloud technology play in product engineering? 

Cloud technology enables scalable, resilient, and cost-effective product development. Cloud-native applications support continuous delivery, faster updates, and easier integrations with other enterprise systems. 

Conclusion 

In today’s rapidly evolving digital landscape, enterprises need to be agile, innovative, and customer-centric to remain competitive. Product engineering provides the tools, processes, and frameworks to achieve this transformation at scale. 

By integrating modern practices—cloud-native development, Agile and DevOps, microservices architecture, and AI integration—enterprises can build digital products that are scalable, secure, and impactful. 

Organizations that invest in strong product engineering capabilities—or partner with experts offering software product engineering services—are better positioned to lead the digital future with confidence and agility. 

For enterprise technology leaders, the message is clear: Product engineering is not just a support function—it is a strategic imperative. 

Managing ELB for a Kubernetes Cluster using AWS Load Balancer Controller

Posted on by Vijendran B

Introduction

Running applications in a Kubernetes cluster has many advantages, including scalability, flexibility, and ease of management. However, to make our applications highly available and resilient, we often need a load balancer to distribute the incoming traffic across multiple pods or nodes. Amazon Web Services (AWS) offers the Elastic Load Balancer (ELB) service, which can be integrated with our Kubernetes cluster to achieve this. This blog post will explore how to manage ELB for a Kubernetes cluster using the AWS Load Balancer Controller.

What is the AWS Load Balancer Controller?

The AWS Load Balancer Controller is an open-source project that simplifies the integration of AWS Elastic Load Balancers with Kubernetes clusters. A Kubernetes Ingress Controller automates the creation and management of AWS load balancers. This controller enables us to define Kubernetes resources like Ingress, Services, and Network Load Balancers as custom resources, making it easy to configure and manage AWS load balancers directly from our Kubernetes cluster.

Prerequisites:

Before we start managing ELBs with the AWS Load Balancer Controller, we should have the following prerequisites in place:

  • An AWS account with appropriate permissions to create and manage load balancers.
  • A running AWS EKS cluster.
  • AWS CLI installed.
  • Kubectl, the Kubernetes command-line tool, installed and configured to access our cluster.
  • Helm, the package manager for Kubernetes, installed. In this example we will be using Helm for hassle-free installation.

Configuring the AWS Load Balancer Controller: 

After installing the controller, we must configure it to manage our AWS load balancers. We can do this by creating an IAM policy, role, and ServiceAccount for the controller, as well as defining the necessary AWS annotations in our Kubernetes resources.


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1. Create an IAM policy granting the controller the necessary permissions to manage AWS       load balancers. We can use the AWS CLI to create this policy.

  1. Run the following command to download the policy document from github.

# curl -o iam-policy.json https://raw.githubusercontent.com/kubernetes-sigs/aws-load-balancer-controller/main/docs/install/iam_policy.json

  • To create an IAM policy named AWSLoadBalancerControllerIAMPolicy for our worker node instance profile, run the following command:

# aws iam create-policy –policy-name AWSLoadBalancerControllerIAMPolicy I am running a few minutes late; my previous meeting is running over.

    –policy-document file://iam-policy.json

2. Create an IAM role and associate the IAM policy with it. Make sure to trust the AWS service account for the controller.

  1. To get the cluster’s OIDC provider URL, run the below command

  # aws eks describe-cluster –name <CLUSTER_NAME> –query “cluster.identity.oidc.issuer” I am running a few minutes late; my previous meeting is running over.

–output text

  • The output will be something like this.

‘oidc.eks.<REGION_CODE>.amazonaws.com/id/EXAMPLE1234OI5DC1234OI5DCEXAMPLE”

  • Next, copy the following contents and Replace <ACCOUNT_ID> with your AWS account ID. Replace <REGION_CODE> with the AWS Region in which the cluster is in. Replace <OIDC_URL> with the output returned in the previous step. After replacing the text, run the modified command to create the load-balancer-role-trust-policy.json file.

# cat >load-balancer-role-trust-policy.json <<EOF

  {

      “Version”: “2012-10-17”,

      “Statement”: [

          {

              “Effect”: “Allow”,

              “Principal”: {

                  “Federated”: “arn:aws:iam::<ACCOUNT_ID>:oidc-provider/<OIDC_URL>”

              },

              “Action”: “sts:AssumeRoleWithWebIdentity”,

              “Condition”: {

                  “StringEquals”: {

                      “<OIDC_URL>: “sts.amazonaws.com”,

                      “<OIDC_URL>:sub”: “system:serviceaccount:kube-system:aws-load-balancer-controller”

                  }

              }

          }

      ]

  }

  EOF

  • Create the IAM role.

    # aws iam create-role I am running a few minutes late; my previous meeting is running over.

      –role-name AmazonEKSLoadBalancerControllerRole I am running a few minutes late; my previous meeting is running over.

      –assume-role-policy-document file://”load-balancer-role-trust-policy.json“

  5. Attach the required Amazon EKS-managed IAM policy to the IAM role. Replace <ACCOUNT_ID> with our AWS account ID.

  # aws iam attach-role-policy \

    –policy-arn arn:aws:iam::<ACCOUNT_ID>:policy/AWSLoadBalancerControllerIAMPolicy \

    –role-name AmazonEKSLoadBalancerControllerRole

3. Installing the AWS load balancer controller add-on

 1. Run the update-kubeconfig AWS command to update the cluster name in the kubeconfig file and confirm that it updates the config file under ~/.kube/config:

      # aws eks –region <REGION_CODE> update-kubeconfig –name cluster_name

2. Create the Kubernetes service account on our cluster. The Kubernetes service account named aws-load-balancer-controller is annotated with the IAM role that was created in the name AmazonEKSLoadBalancerControllerRole.

      # cat >aws-load-balancer-controller-service-account.yaml <<EOF

      apiVersion: v1

      kind: ServiceAccount

      metadata:

        labels:

          app.kubernetes.io/component: controller

          app.kubernetes.io/name: aws-load-balancer-controller

        name: aws-load-balancer-controller

        namespace: aws-load-balancer-controller

        annotations:

          eks.amazonaws.com/role-arn: arn:aws:iam::<ACCOUNT_ID>:role/AmazonEKSLoadBalancerControllerRole

      EOF

 3. Run the below kubectl command to create the service account

      # kubectl apply -f aws-load-balancer-controller-service-account.yaml

4. Install the AWS Load Balancer Controller using Helm V3

    To install the AWS Load Balancer Controller, follow these steps:

  1. First, add the Helm chart repository for the AWS Load Balancer Controller:

# helm repo add eks https://aws.github.io/eks-charts

  • Next, update the Helm repositories:

# helm repo update

  • Create a namespace for the controller (optional but recommended):

# kubectl create namespace aws-load-balancer-controller

  • Install the AWS Load Balancer Controller using Helm. Replace `<CLUSTER_NAME>` with the name of the Kubernetes cluster.

      # helm upgrade -i aws-load-balancer-controller eks/aws-load-balancer-controller \

        –namespace=aws-load-balancer-controller \

        –set clusterName=<CLUSTER_NAME> \

        –set serviceAccount.create=true \

        –set serviceAccount.name=aws-load-balancer-controller

            5. Verify the deployment

      # kubectl get deployment -n aws-load-balancer-controller aws-load-balancer-controller

  • Deploying a nginx image and exposing it as a Clusterip service.

  # Sample Nginx deployment

  apiVersion: apps/v1

  kind: Deployment

  metadata:

    name: nginx

    labels:

      app: nginx

  spec:

    replicas: 1

    selector:

      matchLabels:

        app: nginx

    template:

      metadata:

        labels:

          app: nginx

      spec:

        containers:

        – name: nginx

          image: nginx

          ports:

          – containerPort: 80

  #SVC Exposing as clusterIP

  apiVersion: v1

  kind: Service

  metadata:

    labels:

      app: nginx

    name: nginx

  spec:

    ports:

    – port: 80

      protocol: TCP

    selector:

      app: nginx

Apply the deployment and service configuration by running the kubectl commands

    # kubectl apply -f nginx_deploy.yml

To verify the deployment run the below command

                # kubectl get deployment nginx

  • Adding Ingress routes

Update the Ingress resource with AWS-specific annotations to control how the controller configures the load balancer.

For example, we can specify the load balancer type (e.g., Application Load Balancer or Network Load Balancer) and configure SSL termination, authentication, and other load balancer settings. Here we’ll be using AWS Certificate Manager (ACM) for configuring HTTPS, We need to provide the ACM arn in the annotation “alb.ingress.kubernetes.io/certificate-arn”

Here’s an example of an Ingress resource with AWS annotations:

apiVersion: networking.k8s.io/v1

kind: Ingress

metadata:

  name: my-ingress

  annotations:

    kubernetes.io/ingress.class: alb

    alb.ingress.kubernetes.io/scheme: internet-facing

    alb.ingress.kubernetes.io/certificate-arn: <acm_ssl_arn>

    alb.ingress.kubernetes.io/listen-ports: ‘[{“HTTP”: 80}, {“HTTPS”:443}]’

    alb.ingress.kubernetes.io/actions.ssl-redirect: >-

        {

            “Type”: “redirect”,

            “RedirectConfig”: {

                “Protocol”: “HTTPS”,

                “Port”: “443”,

                “Host”: “#{host}”,

                “Path”: “/#{path}”,

                “Query”: “#{query}”,

                “StatusCode”: “HTTP_301”

            }

        }

spec:

  rules:

    – host: demo-app.example.com

      http:

        paths:

          – path: /

            pathType: Prefix

            backend:

              service:

                name: nginx

                port:

                  number: 80

Once the controller is installed and configured, it will automatically create and manage AWS load balancers based on our Kubernetes resources. This means we can define and update our load balancers directly in our cluster’s YAML files, making it easier to manage our application’s network traffic.

Key benefits of managing ELBs with the AWS Load Balancer Controller:

  • Simplified Configuration: The AWS Load Balancer Controller simplifies the process of creating and managing load balancers in AWS. Kubernetes manifests, such as Ingress resources, can be used to define routing rules, SSL certificates, and other load balancing configurations.
  • Flexibility: We can define and update load balancers as Kubernetes resources, making it easy to scale and modify our application’s network setup.
  • Automation: The controller automates the creation and management of AWS load balancers, reducing manual tasks and the risk of misconfigurations.
  • Autoscaling: As your application scales, the AWS Load Balancer Controller dynamically adjusts the associated AWS resources to handle increased traffic. This ensures that your application remains highly available and responsive.
  • Integration: AWS Load Balancer Controller integrates seamlessly with other AWS services, such as AWS Certificate Manager for SSL certificates and AWS Web Application Firewall for security.
  • Consistency: The controller ensures that our AWS load balancers are consistent with our Kubernetes configuration, reducing the risk of drift.


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Conclusion

Managing elastic load balancers for a Kubernetes cluster using the AWS load balancer controller simplifies the process of load balancer configuration and management. By integrating the controller with our cluster, we can define our load balancers as Kubernetes resources and let the controller handle the rest. This approach streamlines operations, increases automation, and ensures a consistent and reliable network infrastructure for our applications in the AWS cloud.

What is Product Engineering in Software Engineering? — and Why It Matters for Modern Enterprises

Posted on by Indium

In the landscape of modern software development, the term “product engineering” has emerged as a crucial discipline that bridges innovation, technology, and customer-centric delivery. While traditional software development focuses on building and shipping software applications, Product Engineering vs Product Development highlights how the two differ in terms of scope, lifecycle involvement, and business value. 

In this blog, we explore what product engineering means in the context of software engineering, how it differs from traditional software development, its key components, and why it is essential in today’s fast-paced, competitive digital economy.

Understanding Product Engineering in Software Engineering 

Product engineering in software engineering refers to the comprehensive process of designing, developing, testing, deploying, and maintaining a software product with a focus on its functionality, scalability, usability, and business value. It blends engineering principles, modern development practices, and cross-functional collaboration to deliver products that are ready for real-world use and commercial success. 

Unlike one-off software projects or custom solutions, product engineering emphasizes: 

  • Continuous improvement 
  • Long-term maintenance 
  • Scalability for a growing user base 
  • Customer feedback loops 

It includes not only technical implementation but also strategy, design, user experience, and lifecycle management. 

Key Components of Product Engineering 

1. Product Ideation & Planning 

This phase involves business analysts, product managers, and stakeholders who gather requirements, conduct market research, and define the product roadmap. 

2. Architecture & Design 

Architects define the software architecture to ensure scalability, maintainability, and performance. UX/UI teams contribute to the product’s visual and functional design. 

3. Agile Development & Engineering 

Software engineers develop features using Agile methodologies. They prioritize sprints, collaborate across functions, and implement CI/CD pipelines for faster delivery. 

4. Quality Assurance & Testing 

QA teams conduct both manual and automated testing to ensure reliability, performance, and compliance with functional requirements. 

5. DevOps & Deployment 

Engineers use containerization, infrastructure-as-code, and automation tools like Kubernetes and Jenkins to streamline deployment. 

6. User Feedback & Optimization 

Post-launch, product engineers analyze user behavior through tools like Google Analytics, Mixpanel, or Hotjar to enhance performance and user experience. 

7. Lifecycle Management & Support 

Product engineering continues even after launch, with ongoing bug fixes, upgrades, performance improvements, and feature expansions. 

Product Engineering vs. Traditional Software Development 

Criteria Traditional Software Development Product Engineering
Goal Deliver a working software solutionBuild a scalable, market-ready product 
Approach One-time or project-basedIterative, lifecycle-oriented 
Focus FunctionalityFunctionality + User Experience + Scalability 
Stakeholders Developers and clients Cross-functional teams including product managers, designers, QA, DevOps 
Post-launch Involvement Minimal Continuous improvement & support

Why Product Engineering Matters 

Companies that invest in Product Engineering build better software products, launch faster, and iterate effectively—giving them a competitive edge. 

In the modern digital landscape, users expect not just working applications but seamless experiences that solve real problems. Product engineering ensures that software is: 

  • Customer-Centric: Built based on actual user needs and feedback 
  • Scalable: Capable of supporting a growing user base without compromising performance 
  • Secure: Incorporates secure coding practices and compliance standards 
  • Maintainable: Designed for easy updates and new feature integrations 

Companies that invest in product engineering build better software products, launch faster, and iterate effectively—giving them a competitive edge. 

Use Case Example: SaaS Platform Development 

This example demonstrates how a structured engineering approach enhances agility and long-term growth. Businesses seeking to implement similar practices can explore Software Product Engineering Services to gain access to full-lifecycle expertise. 

  • Traditional Development Approach: The team builds a feature-rich app based on initial requirements. After launch, the product stagnates as user feedback is slow to implement. 
  • Product Engineering Approach: 
  • Starts with a lean MVP that solves a core user problem 
  • Gathers user feedback and adjusts roadmap accordingly 
  • Engineers implement modular architecture for easy scaling 
  • QA automates tests to prevent regressions 
  • DevOps ensures seamless releases across environments 
  • Product evolves with monthly releases and growing user demand 

The result? A product that continuously adapts, performs, and delights its users. 

Tools Commonly Used in Product Engineering 

As part of modern engineering stacks, tools like Jenkins, Kubernetes, and analytics platforms not only support delivery but also pave the way for AI-Powered Product Engineering by enabling continuous learning and optimization. 

  • Development & Code Management: GitHub, Bitbucket, GitLab 
  • Testing: Selenium, Postman, TestNG, JMeter 
  • CI/CD & DevOps: Jenkins, Docker, Kubernetes, Azure DevOps 
  • Monitoring: Datadog, New Relic, Prometheus 
  • User Analytics: Mixpanel, Amplitude, Google Analytics 

The Role of a Product Engineer 

A product engineer is more than just a software developer. They: 

  • Collaborate across teams (product, design, QA, ops) 
  • Prioritize user impact and business value 
  • Take ownership of the product’s performance and lifecycle 
  • Stay updated with the latest tech trends and tools 

Their responsibility lies not only in delivering working code but in ensuring that the final product is something that users love and businesses can scale. 

Conclusion 

Product engineering in software engineering is a strategic, integrated approach that focuses on delivering high-quality, scalable, user-centric software products. It combines agile development, modern DevOps, thoughtful design, and data-driven optimization to ensure long-term success. 

Whether you’re a startup or an enterprise, embracing product engineering practices will help you build better software faster and with greater impact. It’s not just about writing code—it’s about engineering products that solve problems, create value, and scale sustainably. 

Want to know how product engineering can transform your software strategy? Explore our Product Engineering Services today. 

Frequently Asked Questions (FAQs) 

1. What is product engineering in software engineering? 

Product engineering in software engineering refers to the end-to-end process of ideating, designing, developing, testing, deploying, and maintaining software products. It combines technical expertise with strategic thinking to deliver scalable and user-centric digital solutions. 

2. How is product engineering different from traditional software development? 

Traditional software development often focuses solely on coding and delivery. In contrast, product engineering encompasses the entire product lifecycle, including UX/UI design, DevOps, quality assurance, continuous improvement, and business alignment. 

3. What skills are essential in a product engineering team? 

Key skills include software architecture, frontend and backend development, DevOps, cloud engineering, user experience design, QA automation, and product management.

4. Why is product engineering important for modern enterprises? 

It ensures that digital products are not only functional but also scalable, maintainable, and aligned with business goals. It supports faster innovation, better user experiences, and improved product-market fit. 

5. Can product engineering be outsourced? 

Yes. Many organizations outsource product engineering to specialized service providers with expertise in full lifecycle software development, helping them scale faster and access broader talent pools. 

AI-Enabled Metrics for Release Decision

Posted on by Premalatha S

Developments in artificial intelligence (AI) can help with the faster, well-informed strategic decision-making process by assessing data, recognizing patterns and variables in complex circumstances, and recommending optimal solutions. The purpose of AI in decision-making is not complete automation. Rather, the goal is to help us make quicker and better decisions through streamlined processes and effective use of data.

In a QA cycle, we capture various metrics to gauge the testing we have done against the baseline values according to industry standards. In this article, we are using an AI model to make the release sign-off decision, calculated with automated metrics.

AI-Enabled Model

AI-based release decision, often referred to as AI model deployment or rollout, involves determining when and under what conditions an AI system should be put into production or made available to end-users. Here are some key considerations for making AI-based release decisions:

Model Evaluation: Before making a release decision, it’s essential to thoroughly evaluate the AI model’s performance using appropriate metrics. This evaluation should include various aspects, such as accuracy, precision, and any other relevant performance indicators. The model should meet predefined quality and accuracy standards.

Here is the AI model designed…

Based on the above, the most important decisions are arrived at, which are mentioned below:

Release Tollgate Decision

This decision entails the criteria for Production Readiness, determining whether to sign off for production or not. The decision is based on the provided values.

Quality Quotient

The Quality Quotient is a percentage derived from established metrics used for assessing and improving software quality. The following parameters are captured, and the quality quotient is determined with a predefined formula. The decision is based on the following range of values: 0% to 98%.

Testing & Validation

Extensive testing is necessary to identify and address potential issues, including edge cases that the AI model might encounter. Testing should cover a wide range of inputs to ensure the system’s robustness. Validation involves verifying that the AI model’s performance aligns with business objectives and requirements to contribute to the desired goals.

Use Cases

This model is evaluated for two projects. One is in the social media domain, which has weekly pushes to production. We have the model with the process of capturing the status of tests and defects through tools like JIRA and qTest. The captured data is fed into a dynamic dashboard with built-in formulas for calculating the metrics needed for sign-off.

The results are greatly helpful in making the release decision. We have some feedback mechanisms which helped to evolve the model and we are recommending the same to the customer.

The second one is for a fortnightly release financial domain project. Here the model gave indicative results for making the release decision.

Release decisions should be data-driven and grounded in a well-defined process that considers the AI system’s technical and business aspects. It’s crucial to strike a balance between delivering AI solutions swiftly and ensuring they adhere to quality, ethical, and security standards. Regularly reviewing and updating the release criteria is essential as the AI system evolves and new information emerges.

The Role of OCR and NLP in Automation Testing

Posted on by Jayachandran V & Divya A & Thiyagarajan K

OCR (Optical Character Recognition) and NLP (Natural Language Processing) are next-generation technologies that can automate data extraction, analyze textual content, improve test case generation, drastically improving the efficiency and effectiveness of automation testing processes.

Understanding OCR

OCR is a technology used to convert scanned documents or images containing text into computer-readable text, allowing automated data extraction and analysis.

Real-life Applications of OCR in Automation Testing

Extracting Data: Extract crucial information like invoice numbers from invoices, receipts, or forms. By using this, we can perform validations, ensuring that software correctly processes and stores such information.

Test Data Generation: Reads test data from legacy systems or documents and creates test scenarios and test cases, reducing manual effort in data preparation.

Example 1: Extract product details, prices, and customer information from invoices and purchase orders. This is used to perform end-to-end testing, ensuring accurate order processing and improving customer experience.

Example 2: Digitize prescriptions and medical reports which are used in automated testing of EHR systems, guaranteeing the correct storage and recovery of patient information, medications, and treatment histories.

Introduction to NLP

NLP is a branch of artificial intelligence that helps computers understand, interpret, and generate human language. Its role is to bridge the gap between human communication and machine understanding, allowing software to process, analyze, and respond to text and speech data in a way that resembles human language comprehension.

Real-Time Examples of NLP in Automation Testing

Log Analysis: Identifies patterns and errors in log data, automates the detection of exceptions, and reduces the need for physical log inspection.

Test Case Generation: Converts natural language requirements into executable test cases. By translating textual descriptions of desired functionalities, NLP streamlines test case creation, ensuring that test cases accurately reflect intended behavior and reducing the time required for test design and scripting.

Chatbot Testing: By simulating user conversations with natural language, NLP ensures the chatbot’s understanding and ability to provide appropriate responses, improving overall functionality and user experience.

Accessibility Testing: Assesses the clarity and correctness of textual content for screen readers and visually impaired users.

Localization Testing: Automatically compares source and target language content to ensure that localized versions of software or websites accurately reflect the original text and cultural requirements for various global audiences.

Integration of OCR and NLP

Combining OCR and NLP in automation testing allows for advanced capabilities, such as extracting and comprehending text from images or documents, enabling sophisticated data validation and test case generation.

Extracting Text from Images: OCR can extract text from images, making content machine-readable. NLP can then analyze the extracted text, allowing automation scripts to validate the information in image-based UI testing.

Sentiment Analysis on User Reviews: NLP can perform sentiment analysis on user reviews, categorizing opinions as positive, negative, or neutral. Combined with OCR, you can extract textual reviews from images or unstructured data sources, enabling automation to assess user sentiment without manual data entry.

Benefits of Using OCR and NLP in Automation Testing

The integration of OCR and NLP minimizes manual effort in data entry and test case generation, allowing testing teams to focus on higher-level tasks. Additionally, these technologies excel at handling complex scenarios, such as analyzing vast amounts of textual and visual data, enhancing test coverage, and overall testing effectiveness.

Conclusion

In conclusion, the synergy of OCR and NLP in automation testing promises a transformative leap in efficiency, accuracy, and coverage, ushering in a new era of software quality assurance where intricate testing challenges can be met with ease, precision, and speed.

How Gen AI-powered portfolio assessment can fine-tune your legacy app’s technology landscape?

Posted on by Uma Raj

Why legacy applications require a makeover?

By 2026, Gartner predicts that over 80% of businesses will have implemented applications with generative AI capabilities or used generative AI APIs.

Application modernization is the strategic upgrade of legacy systems using modern technologies. It is not just about replacing technology; it’s about adopting current development practices like DevOps and infrastructure-as-code. These approaches ensure streamlined collaboration, automation, and efficient resource management, further amplified by generative AI solutions that optimize processes and drive innovation

The treatment of legacy applications can span a spectrum, from rehosting for quick wins to comprehensive rewrites for unlocking the full potential of cloud-native principles. The optimal approach depends on the application’s value, criticality, and desired business outcomes.

While rehosting offers immediate benefits, rewriting unlocks the most significant advantages. It allows building truly cloud-native applications characterized by superior flexibility, rapid development cycles, and seamless scaling. This empowers businesses to respond swiftly to market demands and accelerate innovation.

Why Gen AI for legacy modernization?

Modernizing applications used to be a slog. Laborious manual rewrites, hefty resource demands, and endless timelines defined the process. But the tech landscape is evolving, and businesses are yearning for faster, smarter solutions to bring their applications into the future. This is where Generative AI (Gen AI) emerges as a game-changer, fundamentally reshaping the modernization game. Gen AI analyzes your applications, identifies modernization opportunities, and even generates code suggestions to accelerate the process.

In fact, generative AI is emerging as a critical enabler to drive change in accelerating modernization, making it an essential tool for cost-conscious businesses.

Legacy systems: A bottleneck in modern business

Legacy systems are characterized by a constellation of limitations that impede organizational progress. These limitations can be broadly categorized into inherent shortcomings and operational challenges.

Inherent shortcomings

Obsolescence: Built with outdated technologies and methodologies, legacy systems need more capabilities and security features of modern solutions. This renders them vulnerable to cyber threats and incompatible with modern software and hardware.

Inflexibility: Designed for specific, often narrow purposes, legacy systems need help to adapt to evolving business needs and changing market dynamics. Modifying or extending their functionality is often a cumbersome and costly endeavor.

Performance bottlenecks: Inefficient code and outdated architecture lead to sluggishness, data processing delays, and frustrating user experiences. These limitations can significantly hinder operational efficiency and productivity.

Operational challenges

Security risks: Patching and updating legacy systems can be difficult, if possible, due to compatibility issues and lack of vendor support. This exposes them to known vulnerabilities and increases the risk of data breaches and security lapses.

Limited maintenance: As skilled personnel familiar with the arcane intricacies of legacy systems retire, finding qualified replacements becomes increasingly challenging and expensive. This can reduce maintenance frequency and response times, further exacerbating existing problems.

Scalability constraints: Legacy systems cannot often scale efficiently to meet growing business demands. This can impede expansion, limit market reach, and ultimately stifle growth.

Compliance checks: Complying with evolving regulations and data privacy mandates can be a near-impossible feat with legacy systems. Their rigid structures and opaque data handling practices make it difficult to meet compliance requirements, potentially exposing the organization to legal and financial risks.

Ready to modernize your legacy systems with the power of generative AI?

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Ten ways Gen AI-powered portfolio assessment can fine-tune your legacy app landscape

1. Generate cost-effective roadmaps: With a precise understanding of your app landscape, Gen AI can create personalized modernization roadmaps, considering factors like budget, resource availability, and business priorities. This data-driven approach ensures efficient resource allocation and maximizes the return on your modernization investment.

2. Prioritize modernization candidates: Gen AI can assess the criticality and dependencies of different applications within your portfolio, guiding you in prioritizing which ones to modernize first. This ensures you maximize the return on investment while minimizing disruption to ongoing operations.

3. Predict and prevent risks: Gen AI can analyze historical data and identify potential risks associated with modernization efforts, such as compatibility issues or unexpected performance drops. This allows you to proactively invest in modernization initiatives that align with your long-term business goals and prevent your legacy systems from becoming obsolete.

4. Remove code clutter: Generative AI can detect repetitive logic scattered across your codebase, analyze its purpose, and replace it with a single, centralized function generated by itself. This not only cleans up your code but also reduces complexity and simplifies maintenance.

5. Automate and streamline code generation: Gen AI automates tedious tasks like code analysis and enables you to create a functional document from existing applications, which can be converted into JIRA stories. Moreover, these JIRA stories can be further translated into a modern code base with Gen AI.

6. Uncover bottlenecks and opportunities: Gen AI can analyze vast amounts of data across your legacy applications, identifying underutilized features, performance bottlenecks, and potential security vulnerabilities. This deep dive reveals hidden opportunities for optimization and targeted modernization efforts.

7. Translate to microservices: Buried deep within your legacy code might lurk functionalities wanting to be agile microservices. Generative AI can identify these modules and suggest code segments for isolation, automatically generating the necessary microservice structure and APIs.

8. Detox databases: Outdated databases hinder performance. Generative AI can scan your legacy code, identify database dependencies, and suggest optimal migration paths and schema updates, seamlessly transitioning you to modern SQL or blazing-fast NoSQL solutions.

9. Automate bug fixes: Gen AI can identify and fix bugs, keeping your application running smoothly. GenAI eases integration with modern libraries, generates RESTful APIs, and improves code modularity, future-proofing your app.

10. Modernize user experience: Legacy apps often need help to keep up with modern user expectations. Generative AI can generate user-friendly layouts, create responsive CSS for mobile devices, and even suggest modern design elements—all while preserving core functionality.

Finally, Gen AI sets modernization on autopilot.

By leveraging GenAI-powered portfolio assessment, you can gain a deep understanding of your legacy applications, identify the most impactful modernization opportunities, and make informed decisions about the future of your technology landscape. This data-driven approach allows you to prioritize modernization efforts, maximize your return on investment, and build a future-proof IT infrastructure.

Remember, successful modernization is not just about replacing old technology with new; it’s about understanding your needs, identifying the right opportunities, and implementing solutions that optimize your IT landscape for long-term success.

Take away

Integrate Gen AI into your ongoing application lifecycle management (ALM) to continuously monitor and optimize your modernized app landscape. Ensure your technology landscape remains dynamic and adaptable, constantly evolving to meet your evolving business needs.

Collaboration of Synthetics: ML’s Evolutionary Edge

Posted on by Nizamuddin M

The desire for data is like an endless hole in the world of data and analytics today. The big data analytics business is predicted to reach $103 billion this year, and 181 zettabytes of data will be produced by 2025.

Despite massive data being generated, access to and availability remains a problem. Although public databases partially address this issue, certain dangers are still involved. One of them is bias caused by improper usage of data sets. The second difficulty is requiring different data to train the algorithms and satisfy real-world requirements properly. The quality of the algorithm will also be impacted by data accuracy. It is regulated to preserve privacy and might be expensive to obtain.

These problems can be resolved by using synthetic data, which enables businesses to quickly produce the data sets required to satisfy the demands of their clients. Gartner predicts that by 2030, synthetic data will likely surpass actual data in AI models, even though accurate data is still regarded as superior.

Decoding the Exceptional Synthetic Data

So, what do you get when we say synthetic data? At the forefront of modern data-driven research, institutions like the Massachusetts Institute of Technology (MIT) are pioneering the utilization of synthetic data. Synthetic data refers to artificially generated datasets that mimic real-world data distributions, maintaining statistical properties while safeguarding privacy. This innovative approach ensures that sensitive information remains confidential, as exemplified by MIT’s creation of synthetic healthcare records that retain essential patterns for analysis without compromising patient privacy. This technique’s relevance extends to various domains, from machine learning advancements to societal insights, offering a powerful tool to unlock valuable knowledge while upholding data security and ethical considerations.

Using synthetic data, new systems can be tested without live data or if the data is biased. Small datasets not being used can be supplemented, and the accuracy of learning models can be improved. Synthetic data can also be used when real data cannot be used, shared, or moved. It can create prototypes, conduct product demos, capture market trends, and prevent fraud. It can even be used to generate novel, futuristic conditions.

Most importantly, it can help businesses comply with privacy laws, mainly health-related and personal data. It can reduce the bias in data sets by providing diverse data that reflects the real world better.

Use Cases of Synthetic Data

Synthetic data can be used in different industries for different use cases. For instance, computer graphics and image processing algorithms can generate synthetic images, audio, and video that can be used for training purposes.

Synthetic text data can be used for sentiment analysis or for building chatbots and machine translation algorithms. Synthetically generated tabular data sets are used in data analytics and training models. Unstructured data, including images, audio, and video, are being leveraged for speech recognition, computer vision, and autonomous vehicle technology. Financial institutions can use synthetic data to detect fraud, manage risks, and assess credit risk. In the manufacturing industry, it can be used for quality control testing and predictive maintenance.

Also read: The Transformative Impact Of Generative AI On The Future Of Work.

Generating Synthetic Data
How synthetic data is generated will depend on the tools and algorithms used and the use case for which it is created. Three of the popular techniques used include:

Technique #1 Random Selection of Numbers: One standard method is randomly selecting numbers from a distribution. Though this may not provide insights like real-world data, the data distribution matches it closely.

Technique #2 Generating Agent-based Models: Unique agents are created using simulation techniques to enable them to communicate with each other. This is especially useful in complex systems where multiple agents, such as mobile phones, apps, and people, are required to interact with each other. Pre-built core components and Python packages such as Mesa are used to develop the models quickly, and a browser-based interface is used to view them.

Technique #3 Generative Models: Synthetic data replicating real-world data’s statistical properties or features is generated using algorithms. Training data learns the statistical patterns and relationships in the data and generates new synthetic data similar to the original. Generative adversarial networks and variational autoencoders are examples of generative models.

The model quality should be reliable to ensure the quality of synthetic data. Additional verification is required and involves comparing the model results with the real-world data that has been annotated manually. Users must be sure that the synthetic data is not misleading, reliable, and 100% fail-safe for privacy.

Synthetic Data with Databricks

Databricks offers dbldatagen, a Python library, to generate synthetic data for testing, creating POCs, and other uses such as Delta Live Tables pipelines in Databricks environments. It helps to:

● Create unique values for a column.
● Allow templated text generation based on specifications.
● Generate data from a specific set of values.
● Generate weighted data in case of repeating values.
● The data generated in a data frame can be written to storage in any format.
● Billions of rows of data can be generated quickly.
● A random seed can be used to generate data based on the value of other fields.


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Real-Time Data Analysis and its Impact on Healthcare

Posted on by Kavitha V Amara

In the grand scheme of things, it’s becoming increasingly evident that data is the new black gold. Industries across the board are awakening to the realization that data is no longer just an afterthought or an add-on; it’s an essential component of success. In the 19th century, oil was the lifeblood of the global economy and politics. In the 21st century, data is controlled to take on the same critical role.

Of course, data in its raw and unrefined form is essentially useless. It’s only when data is skillfully gathered, integrated, and analyzed that it starts to unlock its actual value. This value can manifest in many ways, from enhancing decision-making capabilities to enabling entirely new business models. In the healthcare industry, data is playing a particularly pivotal role. Refined data is helping professionals make better-informed decisions, improve patient outcomes, and unlock new frontiers of medical research. The future of healthcare is all about data, and those who know how to wield it will undoubtedly emerge as leaders in the field.

However, healthcare providers’ timely access to real-time or just-in-time information can significantly enhance patient care, optimize clinician efficiency, streamline workflows, and reduce healthcare costs.

Investing in robust electronic health record (EHR) systems encompassing all clinical data is crucial for healthcare organizations to understand patient conditions and comprehensively predict patient outcomes.

Is Data a Real Game Changer in the Healthcare Industry?

The answer to whether the analytical application of existing data will shape the future of healthcare is a resounding “yes.” With advances in data-collecting tools and healthcare technology, we’re witnessing a new era of healthcare delivery that will revolutionize the industry.

Imagine a world where wearable medical devices warn you of potential health risks or medical advice apps offer personalized guidance based on your unique DNA profile. These are just a few examples of how cutting-edge technology is making its way into the healthcare space, enabling data-driven decisions that improve patient outcomes and drive down costs.

Real-time data is a game-changer for case review and clinical time management, allowing healthcare professionals to understand patient situations and forecast outcomes more effectively. To fully realize the potential of data-driven healthcare, healthcare organizations must implement robust data management systems that can store all clinical data and provide the necessary tools for data analysis. By doing so, healthcare professionals will be empowered to make informed decisions that enhance patient care, improve outcomes, and ultimately transform the healthcare landscape.

Also, read the best approach to testing digital healthcare.

How do you use data for a better future?

When it comes to healthcare, data is everything. However, with the massive amounts of data that healthcare professionals must contend with, the sheer volume of information can be overwhelming.
As the industry has shifted toward electronic record keeping, healthcare organizations have had to allocate more resources to purchasing servers and computing power to handle the influx of data. This has led to a significant surge in spending across the sector.

Despite the clear advantages of data-driven healthcare, managing such large amounts of information presents unique challenges. Sorting through and making sense of the data requires robust data management systems and advanced analytical tools. However, with the right approach, healthcare professionals can leverage this data to make informed decisions that improve patient outcomes and transform the industry.

How does data analytics benefit the healthcare industry?

A small diagnostic error can have devastating consequences in the healthcare industry, potentially costing lives. The difference between an actual positive malignant tumor and a benign one can be the difference between life and death. This is where data analytics comes into play, helping to eliminate the potential for error by identifying the most relevant patterns in the available data and predicting the best possible outcome.

Beyond improving patient care, data analytics can also assist hospital administration in evaluating the effectiveness of their medical personnel and treatment processes. As the industry continues to shift toward providing high-quality and reasonable care, the insights derived from data analysis can help organizations stay on the cutting edge of patient care.

With data analytics, healthcare professionals can harness the power of big data to identify patterns and trends, predict patient outcomes, and improve the overall quality of care. Healthcare organizations can optimize their processes by leveraging data-driven insights, minimizing errors, and ultimately delivering better patient outcomes.

Approaches of Data Analytics

Data analytics is a complex process involving various approaches, E.g., predictive analysis, descriptive analysis, and prescriptive analysis, including feature understanding, selection, cleaning, wrangling, and transformation. These techniques are applied depending on the type of data being analyzed.

Analysts must first understand the features and variables relevant to the analysis to derive insights from the data. From there, they can select the most relevant features and begin cleaning and wrangling the data to ensure accuracy and completeness.

Once the data has been prepared, analysts can apply various transformation techniques to derive insights and patterns. The specific methods used will depend on the nature of the data being analyzed but may include methods such as regression analysis, clustering, and decision trees.

Predictive Analysis

Analysts leverage sophisticated techniques such as relational, dimensional, and entity-relationship analysis methodologies to forecast outcomes. By applying these powerful analytical methods, they can extract insights from large and complex datasets, identifying patterns and relationships that might otherwise be obscured.

Whether analyzing patient data to forecast disease progression or studying market trends to predict demand for new medical products, these advanced analytical techniques are essential for making informed decisions in today’s data-driven world. By leveraging the latest tools and techniques, healthcare professionals can stay ahead of the curve, improving patient outcomes and driving innovation in the industry.

Descriptive Analysis

In the data analytics process, descriptive analysis is a powerful technique that can be used to identify trends and patterns in large datasets. Unlike more complex analytical methods, descriptive analysis relies on simple arithmetic and statistics to extract insights from the data.

Analysts can gain a deeper understanding of data distribution by analyzing descriptive statistics such as mean, median, and mode, helping to identify common trends and patterns. This information is invaluable during the data mining phase, assisting analysts to uncover hidden insights and identify opportunities for further analysis.

Prescriptive Analysis

In data analytics, prescriptive analysis represents the pinnacle of analytical techniques. Beyond simple descriptive or predictive analysis, prescriptive analysis offers recommendations for proceeding based on insights gleaned from the data.

This highly advanced analysis is the key to unlocking new opportunities in the healthcare industry, enabling professionals to make more informed decisions about everything from treatment protocols to resource allocation. By leveraging sophisticated algorithms and machine learning techniques, prescriptive analysis can identify the optimal path forward for any situation, helping organizations optimize processes, maximize efficiency, and drive better patient outcomes.

Gathering Real-time Data in Healthcare

Real-time data refers to data that is immediately obtained upon its creation and can be collected using various methods, including:

  • Health Records
  • Prescriptions
  • Diagnostics Data
  • Apps and IoTs

Real-time data is crucial for managing the healthcare industry’s patient care, operations, and staffing routines. By leveraging real-time data, the industry can optimize its entire IT infrastructure, gaining greater insight and understanding of its complex networks.

Examples of Real-time Data Technologies in Healthcare

Role of AI/ML in healthcare

Regarding medical diagnostics, the power of data analytics cannot be overstated. Thanks to cutting-edge machine learning and deep learning methods, it’s now possible to analyze medical records and predict future outcomes with unprecedented precision.

Take machine learning, for example. By leveraging this technology, medical practitioners can reduce the risk of human error in the diagnosis process while also gaining new insights into graphic and picture data that could help improve accuracy. Additionally, analyzing healthcare consumption data using machine learning algorithms makes it possible to allocate resources more effectively and reduce waste.

But that’s not all. Deep learning is also a game-changer in the fight against cancer. Researchers have achieved remarkable results by training a model to recognize cancer cells using deep neural networks. By feeding the model a wealth of cancer cell images, it could “memorize” their appearance and use that knowledge to detect cancerous cells in future images accurately. The potential for this technology to save lives is truly staggering.

RPA (Robotic process automation) in healthcare

The potential for RPA in healthcare is fascinating. By scanning incoming data and scheduling appointments based on a range of criteria like symptoms, suspected diagnosis, doctor availability, and location, RPA can dramatically boost efficiency. This would relieve the burden of time-consuming scheduling tasks from the healthcare staff and probably improve patient satisfaction.

In addition to appointment scheduling, RPA can also be used to speed up health payment settlements. By consolidating charges for different services, including testing, medications, food, and doctor fees, into a single, more straightforward payment, healthcare practitioners can save time and avoid billing errors. Plus, if there are any issues with cost or delays, RPA can be set up to email patients with customized reminders.

But perhaps the most exciting use of RPA in healthcare is data analysis. By leveraging this technology to produce insightful analytics tailored to each patient’s needs, healthcare providers can deliver more precise diagnoses and treatment plans. Ultimately, this can lead to better outcomes and an enhanced patient care experience.

Role of Big Data in Healthcare

In today’s world, the healthcare industry needs an innovation that can empower medical practitioners to make informed decisions and ultimately enhance patient outcomes. Big data is the transformative force that can revolutionize how we approach healthcare. With the ability to analyze massive amounts of data from various sources, big data can provide medical practitioners with the insights they need to understand better and treat diseases. By leveraging this data, doctors can develop more targeted treatments and therapies that have the potential to improve patient outcomes drastically.

Beyond the immediate benefits of improved treatment options, big data also plays a vital role in driving new drug development. Through advanced clinical research analysis, big data can predict the efficacy of potential new drugs, making it easier for scientists to identify the most promising candidates for further development. This is just one example of how big data is revolutionizing the way we approach healthcare, and the benefits will only continue to grow as we explore more ways to harness its power.

Finally, big data is helping healthcare practitioners to create focused treatments that are tailored to improve population health. By analyzing population health data, big data can detect patterns and trends that would be impossible to identify through other means. With this information, medical professionals can develop targeted treatments that can be applied on a large scale, ultimately improving health outcomes for entire populations. This is just one of the many ways that big data is changing the way we approach healthcare, and it’s clear that the possibilities are endless. As we continue to explore this transformative technology, there’s no doubt that we’ll discover even more innovative ways to leverage big data to improve health outcomes for patients around the world.

Wrapping Up

In conclusion, real-time data analysis is a transformative force in the healthcare industry that has the potential to revolutionize the way we approach patient care. With the ability to analyze vast amounts of data in real-time, medical practitioners can make faster and more informed decisions, resulting in improved patient outcomes and ultimately saving lives.

From predicting potential health risks to identifying disease outbreaks and monitoring patient progress, real-time data analysis is driving innovation in healthcare and changing the way medical professionals approach treatment. By leveraging cutting-edge technologies and advanced analytics tools, healthcare organizations can collect and analyze data from various sources, including wearable devices, electronic health records, and social media, to better understand patient needs and provide personalized care.

As the healthcare industry continues to evolve, it’s clear that real-time data analysis will play an increasingly important role in delivering better health outcomes for patients worldwide. Real-time data analysis can improve patient care, reduce costs, and save lives by giving medical practitioners the insights they need to make more informed decisions. The possibilities for the future of healthcare services are endless, and I’m excited to see the continued innovations that will arise from this transformative technology.

Top 7 Challenges in Product Engineering and How to Overcome Them

Posted on by Indium

Product engineering plays a vital role in building scalable, innovative, and high-performance software products. However, the journey from concept to execution is filled with challenges that can hinder delivery timelines, reduce product quality, or lead to misaligned outcomes. Identifying and proactively addressing these issues is key to ensuring successful and efficient product engineering. Many organizations are turning to trusted software product engineering services to navigate these challenges and accelerate product delivery with greater precision and agility. 

In this blog, we explore the top 7 challenges faced by engineering teams and practical strategies to overcome them—supported by industry best practices, tools, and insights that can empower engineering leaders to build resilient, future-ready solutions. 

1. Legacy System Constraints 

Challenge: 

Many organizations continue to rely on outdated legacy systems that slow down product development, limit integration, and introduce security risks. These systems often lack documentation, are expensive to maintain, and cannot scale effectively with modern needs. 

Solution: 

  • Prioritize legacy modernization using microservices and API-first architecture for flexibility and maintainability. 
  • Implement cloud migration to benefit from elastic infrastructure, scalability, and lower total cost of ownership. 
  • Use containerization and orchestration tools like Docker and Kubernetes to abstract legacy dependencies and simplify deployment. 

Proactively auditing and mapping your current architecture is a vital first step in developing a realistic modernization roadmap. 

2. Siloed Collaboration Across Teams 

Challenge: 

Engineering success requires close coordination between product owners, developers, QA teams, and designers. In siloed environments, poor communication results in duplicated efforts, lack of accountability, and delayed releases. 

Solution: 

  • Adopt Agile and DevOps to promote collaboration, transparency, and shared responsibility. 
  • Leverage collaboration platforms like Jira, Confluence, Slack, and Figma to keep all teams aligned. 
  • Schedule regular planning meetings, daily standups, and sprint reviews to keep feedback loops tight and actions clear. 

Cross-functional alignment improves product quality and ensures teams are building towards the same vision. 

3. Incomplete or Changing Requirements 

Challenge: 

Unclear, changing, or incomplete product requirements cause development rework, misaligned expectations, and inefficiencies in the delivery pipeline. This is especially common in fast-changing industries like fintech and healthtech. 

Solution: 

  • Start with user personas, journey maps, and problem statements to understand true user needs. 
  • Use user stories, acceptance criteria, and wireframes to document and validate expectations clearly. 
  • Maintain an iterative mindset by refining the backlog continuously through sprint reviews and feedback sessions. 

Incorporating end-user feedback early and often helps keep product scope realistic and business-aligned. 

4. Talent Shortage and Skill Gaps 

Challenge: 

Finding engineering talent with up-to-date skills in cloud, AI/ML, cybersecurity, and modern frontend/backend frameworks remains a widespread problem. It slows down development and limits innovation. 

Solution: 

  • Build a culture of continuous learning using LMS platforms, tech workshops, and certification programs. 
  • Partner with engineering service providers to scale niche capabilities on demand. 
  • Embrace low-code/no-code platforms for simple automation, reducing pressure on core developers. 

Focusing on internal mobility and team enablement also strengthens organizational resilience in the face of attrition. 

5. Quality Assurance Bottlenecks 

Challenge: 

Manual testing alone cannot keep pace with modern release cycles. As complexity grows, testing backlogs, inconsistent coverage, and missed bugs become major blockers to product quality. 

Solution: 

  • Use modern testing tools like Selenium, TestNG, Cypress, and Playwright. 
  • Adopt shift-left testing so defects are found earlier during development. 
  • Incorporate AI-based testing tools to prioritize test cases and auto-generate test scripts based on historical defects. 

Investing in robust QA frameworks ensures confidence in faster deployments. 

6. Security and Compliance Risks 

Challenge: 

As cyber threats increase and regulatory frameworks tighten, engineering teams must embed security throughout the product lifecycle. Compliance lapses can result in hefty fines and reputational damage. 

Solution: 

  • Integrate DevSecOps so security is enforced during coding, testing, and deployment phases. 
  • Use automated tools like SonarQube, Snyk, and OWASP ZAP to perform code analysis and vulnerability scanning. 
  • Stay updated with standards like GDPR, HIPAA, ISO 27001, and ensure architectural decisions align with regulatory needs. 

Security must shift from a checkpoint to a continuous concern embedded within engineering culture. 

7. Scalability and Performance Challenges 

Challenge: 

High user traffic, data volume, or real-time processing demands can quickly break systems that weren’t designed to scale. Poor performance leads to downtime, churn, and missed revenue. 

Solution: 

  • Architect products using scalable cloud-native patterns like serverless, containerization, and microservices. 
  • Monitor product health using APM (Application Performance Monitoring) tools such as New Relic, Datadog, and Grafana. 
  • Perform regular load testing, chaos engineering, and capacity planning to stress-test systems. 

Scalability should be part of the product roadmap—not an afterthought. 

Conclusion 

Product engineering is a strategic function that demands foresight, adaptability, and strong cross-functional collaboration. By understanding and proactively addressing the top challenges—legacy constraints, collaboration gaps, scope creep, skill shortages, testing inefficiencies, security threats, and scalability issues—enterprises can build robust, customer-centric digital products. 

Organizations that invest in modern tooling, continuous learning, and agile processes are better positioned to respond to evolving customer needs, deliver faster, and maintain a competitive edge. 

Ultimately, success in product engineering comes from building not just code—but a culture of excellence. 

Frequently Asked Questions (FAQs) 

1. What are common challenges in product engineering? 

Common challenges include dealing with legacy systems, siloed collaboration, unclear requirements, talent shortages, QA bottlenecks, compliance risks, and scaling issues. 

2. How can we improve cross-functional collaboration?

Adopting Agile/DevOps, aligning on tools like Jira and Slack, and running frequent sync meetings help break down silos. 

3. Why is automation important in product engineering? 

Automation enhances speed, repeatability, and accuracy in development, testing, and deployment, reducing time-to-market. 

4. What is shift-left testing? 

Shift-left testing introduces testing activities early in the SDLC, helping to detect and resolve issues before they escalate.

5. Can outsourcing help with product engineering challenges? 

Yes, outsourcing provides access to niche skills, flexible team scaling, and faster delivery—especially for startups or during transformation phases.

6. How can we ensure scalability from the start? 

Design using microservices, cloud-native infrastructure, and performance monitoring tools. Plan for capacity, not just functionality. 

The Future of Product Engineering: Trends Shaping 2025 and Beyond

Posted on by Indium

In an era defined by rapid digital transformation, product engineering has evolved from a back-end technical process into a strategic driver of business growth and innovation. With shifting user expectations, emerging technologies, and a need for speed and agility, product engineering is entering a new chapter—one that prioritizes continuous innovation, customer-centricity, and resilience. As part of this evolution, many organizations are leveraging software product engineering services to streamline development, reduce costs, and bring scalable solutions to market faster. 

As we look ahead to 2025 and beyond, several transformative trends are reshaping how digital products are conceptualized, developed, and delivered. This blog explores the future of product engineering, identifying the key trends and technologies that will define the next wave of innovation and what businesses must do to stay competitive. 

1. AI-Driven Engineering Will Lead the Way 

Artificial Intelligence (AI) is already enhancing various stages of the software development lifecycle, but by 2025, AI will become central to AI-Powered Product Engineering. From design and coding to testing and post-deployment optimization, AI and machine learning will automate repetitive tasks and empower teams to build smarter, faster, and more personalized products. 

Key Developments: 

  • AI-assisted code generation (e.g., GitHub Copilot) 
  • Predictive analytics for feature prioritization 
  • AI-based test automation and bug detection 
  • Personalized user experience through behavioral modeling 

Impact: Teams will improve efficiency, reduce human error, and make data-informed decisions that enhance product outcomes. 

2. Low-Code and No-Code Development Will Accelerate Delivery 

Low-code/no-code platforms are gaining popularity as organizations seek faster ways to deliver digital solutions. While traditionally viewed as tools for internal apps or non-developers, by 2025, these platforms will be essential components of enterprise product engineering strategies. 

Benefits: 

  • Rapid prototyping and MVP development 
  • Easier collaboration between business users and developers 
  • Cost and time efficiency in building standard workflows 

Note: Low-code is not a replacement for software engineers but a complement—freeing them to focus on complex, high-impact features. 

3. Cloud-Native and Edge Computing Will Become the Standard 

The future of product engineering lies in cloud-native development and edge computing architectures. Cloud-native applications offer scalability, portability, and resilience, while edge computing enhances speed and reduces latency by processing data closer to the user. 

Technologies to Watch: 

  • Kubernetes for container orchestration 
  • Microservices and serverless computing 
  • Edge platforms like AWS IoT Greengrass and Azure Stack 

Result: Applications will become more modular, reliable, and performance-optimized, especially for real-time and IoT-heavy use cases. 

4. Human-Centered Design Will Be Embedded in Engineering Culture 

Design is no longer just a front-end concern—it’s a key component of successful product engineering. By 2025, Human-Centered Design (HCD) will become foundational, ensuring that products are built around real user needs and inclusive experiences. 

Core Principles: 

  • Empathy-first development 
  • Accessibility and inclusivity by default 
  • Design systems integrated with engineering workflows 

Impact: Businesses that prioritize user emotions and experiences will differentiate themselves in crowded markets. 

5. Security and Compliance Will Be Integrated from Day One 

With increasing regulatory pressure (GDPR, HIPAA, CCPA) and rising cyber threats, security can no longer be an afterthought. The DevSecOps movement is integrating security into every phase of product development. 

Emerging Practices: 

  • Security as Code 
  • Automated compliance audits 
  • Threat modeling during design 

Why It Matters: Products that embed security and privacy into their DNA will gain customer trust and minimize legal risk. 

6. Hyper-Personalization Through Real-Time Data Engineering 

The demand for personalized digital experiences is growing. By 2025, hyper-personalization—powered by real-time data, machine learning, and AI—will become a competitive necessity in product engineering. 

Use Cases: 

  • Dynamic user interfaces based on behavior 
  • AI-powered product recommendations 
  • Personalized onboarding and notifications 

Key Requirement: Strong alignment between product engineering, data engineering, and customer analytics teams. 

7. Sustainable Engineering Will Gain Business and Ethical Importance 

Sustainability will shape not only product marketing but also product design and engineering. From energy-efficient code to eco-friendly infrastructure decisions, green engineering practices will gain traction. 

Strategies: 

  • Optimizing app performance to reduce power consumption 
  • Selecting cloud providers with renewable energy commitments 
  • Extending product lifecycles to reduce digital waste 

Outcome: Sustainable practices will reduce operational costs and align with ESG goals. 

8. API-First Development and Composable Architecture Will Dominate 

Modern product engineering teams are shifting toward API-first development and composable architecture to promote flexibility and reusability. 

Benefits: 

  • Faster integration with third-party systems 
  • Headless application development 
  • Better scalability and feature modularity 

Trend Example: Using GraphQL and RESTful APIs to decouple services, making it easier to update parts of a product independently. 

9. Rise of Software Product Engineering Services 

As product teams face increasing demands for speed, personalization, and integration, understanding and addressing key Challenges in Product Engineering becomes essential to building resilient systems and delivering seamless digital experiences. 

Services Include: 

  • Full product lifecycle engineering 
  • UI/UX design integration 
  • DevOps and cloud enablement 
  • Continuous delivery and testing 

Why It’s Growing: Companies need partners who can handle end-to-end product development while offering the agility to adapt to fast-changing market dynamics. 

10. Continuous Delivery and Iterative Innovation Will Define Success 

By 2025, software products will be treated as continuously evolving entities. The emphasis will shift from large, infrequent releases to continuous delivery, with teams deploying features, testing updates, and rolling back changes seamlessly. 

Tactics: 

  • Feature flagging for risk-free releases 
  • Real-time feedback collection and telemetry 
  • A/B testing and progressive rollouts 

Impact: Products will remain aligned with evolving user needs and market trends. 

Preparing for the Future: What Product Teams Must Do 

To stay ahead of these trends, businesses must: 

1. Invest in Upskilling: Equip engineering teams with training in AI, low-code platforms, and cloud technologies. 

2. Break Down Silos: Foster cross-functional collaboration between design, data, engineering, and business teams. 

3. Adopt Agile and DevOps Cultures: Streamline feedback loops and promote rapid experimentation.

4. Embrace Customer Feedback Loops: Implement systems that gather and act on user feedback in real time. 

5. Prioritize Ethics and Sustainability: Make security, inclusivity, and eco-conscious design core principles of product engineering. 

Conclusion: Engineering for Impact in 2025 and Beyond 

The future of product engineering is bold, dynamic, and user-driven. The next wave of digital products will not only need to be functional and scalable—but also personalized, ethical, and constantly evolving. This is precisely Why Product Engineering must evolve from a purely technical function into a strategic business capability, tightly aligned with customer needs, market shifts, and innovation cycles. 

By embracing these emerging trends and working with trusted software product engineering services, businesses can lead the market—not just keep up with it. 

Is your organization ready to engineer the future? Discover how our Software Product Engineering Services can help you build resilient, customer-centric digital products built for tomorrow’s world. 

Frequently Asked Questions (FAQs) 

1. What are the top product engineering trends expected in 2025? 

Key trends include AI-driven development, low-code/no-code platforms, edge computing integration, hyper-personalization, and sustainability-focused engineering practices. 

2. How will AI impact product engineering in the future? 

AI will automate testing, assist with code generation, enable predictive maintenance, and enhance user experiences through intelligent personalization and analytics.

3. Are low-code and no-code platforms replacing traditional engineering? 

Not entirely. These platforms accelerate prototyping and business automation but still rely on professional engineers for scalability, security, and complex logic development. 

4. What role does sustainability play in modern product engineering? 

Engineering teams are increasingly considering energy efficiency, ethical sourcing, and carbon footprint in product design and infrastructure to meet ESG and regulatory goals. 

5. How can enterprises prepare for the future of product engineering? 

By investing in continuous learning, modern tech stacks, agile processes, and partnerships with forward-thinking product engineering providers who embrace innovation and flexibility.