#AI Into DevOps Techniques

[public-cloud-computing]

Uncover the revolutionary synergy between AI and DevOps infrastructure in this in-depth analysis. Explore transformative insights for unparalleled efficiency. Dive into the future of seamless collaboration!

[rubylogan15]

Uncover the evolution of DevOps with AI's transformative touch. Dive deep into a comprehensive analysis, unlocking the future of agile infrastructure. Engage with innovation in every byte!

[paulcook159-blog]

Explore the transformative synergy of AI and DevOps, driving innovation and efficiency in software development. Unleash the power of intelligent automation.

[dieterziegler159]

Explore the transformative synergy of AI and DevOps, driving innovation and efficiency in software development. Unleash the power of intelligent automation.

[enterprise-cloud-services]

Explore the transformative synergy of AI and DevOps, driving innovation and efficiency in software development. Unleash the power of intelligent automation.

[goodoldbandit]

How to Use Telemetry Pipelines to Maintain Application Performance.

Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in Optimize application performance with telemetry pipelines—enhance observability, reduce costs, and ensure security with efficient data processing. 🚀 Discover how telemetry pipelines optimize application performance by streamlining observability, enhancing security, and reducing costs. Learn key strategies and best…

[thara333]

Pioneering the Future of Software Quality Assurance through Automation Testing

Automation testing, a dynamic and essential software quality assurance technique, is reshaping the landscape of application testing. Its mission is to execute predefined test cases on applications, delivering heightened accuracy, reliability, and efficiency by automating tedious and time-consuming testing tasks.

The Present and Future Synergy in Automation Testing:

At its essence, automation testing responds to the pressing need for a streamlined testing process. Beyond its current significance, the future promises a paradigm shift, characterized by transformative trends and advancements.

Unveiling Future Trends in Automation Testing:

Proactive "Shift-Left" Testing: Embracing a proactive "Shift-Left" approach, the future of automation testing integrates testing earlier in the development life cycle. This strategic shift aims to detect and address issues at their inception, fostering a more resilient and efficient software development process.

Harmonizing with DevOps: Automation is positioned to become increasingly integral to DevOps practices. Its seamless integration into continuous integration and delivery (CI/CD) pipelines ensures not just faster but more reliable releases, aligning seamlessly with the agile principles of DevOps.

AI and Machine Learning Synergy: The convergence of artificial intelligence (AI) and machine learning (ML) is poised to revolutionize automation testing. This integration enhances script maintenance, facilitates intelligent test case generation, and empowers predictive analysis based on historical data, ushering in a new era of adaptive and efficient testing processes.

Evolving Cross-Browser and Cross-Platform Testing: In response to the diversification of the software landscape, automation tools are evolving to provide robust solutions for cross-browser and cross-platform testing. Ensuring compatibility across diverse environments becomes paramount for delivering a seamless user experience.

Codeless Automation Revolution: The ascent of codeless automation tools represents a pivotal shift in testing methodologies. This trend simplifies testing processes, enabling testers with limited programming skills to create and execute automated tests. This democratization of testing accelerates adoption across teams, fostering a collaborative testing environment.

Concluding the Journey: Navigating Future Imperatives:

In conclusion, automation testing transcends its current role as a necessity, emerging as a future imperative in the ever-evolving landscape of software development. As technologies advance and methodologies mature, automation testing is poised to play a pivotal role in ensuring the delivery of high-quality software at an accelerated pace. Embracing these future trends, the software industry is set to embark on a transformative journey towards more efficient, adaptive, and reliable testing processes.

[gqattech]

GQAT Tech’s QA Arsenal: Powerful Tools That Make Quality Assurance Smarter

In this technology-driven age, delivering high-quality software is not an optional function but a necessity. Clients now expect us to produce digital products— apps, websites, etc.-- that are made quickly and error-free. The best way to achieve this is with a quality assurance (QA) process and effective tools to adapt to their needs.

The GQAT Tech QA team calls their QA efficient and effective tools combined, and their QA arsenal productive platforms, automation, tools, and proven processes. These testing tools and approaches can help a QA team find bugs more quickly, decrease hands-on effort, and allow more test coverage depending on the software project.

Now, let's look at what a QA Arsenal is, why it is important, and how QA will help your business produce better software.

What is the QA Arsenal?

The "QA Arsenal" refers to the collection of tools, frameworks, and strategies at GQAT Tech that the QA team employs to provide quality testing to clients.  The QA Arsenal is like a toolbox, where everything a tester needs to complete the project is in the toolbox. 

It includes:

Automation testing tools

Manual testing techniques

Defect tracking systems

Performance testing platforms

Mobile and web testing tools

CI/CD integrations

Reporting and analytics dashboards

What Makes GQAT’s QA Arsenal Unique?

We do not use tools haphazardly. We use the most appropriate tools based on the client's type of project, technology stack, service timeline, and quality. The QA Arsenal is designed to offer flexibility and therefore considers: 

Web apps

Mobile apps

Embedded systems

IoT devices

Enterprise software

Whether functional test, compatibility test, API test, performance test, GQAT Tech utilizes a custom mixture of tools to ensure that it is reliable, secure, and ready for launch.

Tools Included in the QA Arsenal

Here are some common tools and platforms GQAT Tech uses:

🔹 Automation Tools

Selenium – For web application automation

Appium – For mobile app automation

TestNG / JUnit – For running and managing test cases

Robot Framework – For keyword-driven testing

Postman – For API testing

JMeter – For performance and load testing

🔹 Defect & Test Management

JIRA – To log, track, and manage bugs

TestRail / Zephyr – For test case management

Git & Jenkins – For CI/CD and version control

BrowserStack / Sauce Labs – For cross-browser and device testing

How It Helps Clients

Using the QA Arsenal allows GQAT Tech to:

Detect Bugs Early – Catch issues before they reach end-users

Save Time – Automation reduces time spent on repetitive tasks

Test on Real Devices – Ensures compatibility across systems

Generate Reports – Easy-to-read results and test status

Integrate with DevOps – Faster release cycles and fewer rollbacks

Improve Product Quality – Fewer bugs mean a better user experience

Real Results for Real Projects

GQAT Tech’s QA Arsenal has been successfully used across different domains like:

FinTech

Healthcare

E-commerce

Travel & Transport

EdTech

AI and IoT Platforms

With their domain expertise and knowledge of tools, they help businesses go faster, mitigate risks, and build customer diligence.

Conclusion

Building a great QA team is essential, but having them equipped with the right tools makes all the difference. GQAT Tech’s QA Arsenal provides their testers with everything they need to test faster, smarter, and more comprehensively.

If you are building a digital product and want to ensure it is released in the real world, you need a testing partner who does not leave things to chance. You need a testing partner with a battle-tested QA arsenal.

💬 Ready to experience smarter quality assurance?

👉 Explore GQAT Tech’s QA Arsenal and get in touch with their expert team today!

[hawkstack]

Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI268)

As AI and Machine Learning continue to reshape industries, the need for scalable, secure, and efficient platforms to build and deploy these workloads is more critical than ever. That’s where Red Hat OpenShift AI comes in—a powerful solution designed to operationalize AI/ML at scale across hybrid and multicloud environments.

With the AI268 course – Developing and Deploying AI/ML Applications on Red Hat OpenShift AI – developers, data scientists, and IT professionals can learn to build intelligent applications using enterprise-grade tools and MLOps practices on a container-based platform.

🌟 What is Red Hat OpenShift AI?

Red Hat OpenShift AI (formerly Red Hat OpenShift Data Science) is a comprehensive, Kubernetes-native platform tailored for developing, training, testing, and deploying machine learning models in a consistent and governed way. It provides tools like:

Jupyter Notebooks

TensorFlow, PyTorch, Scikit-learn

Apache Spark

KServe & OpenVINO for inference

Pipelines & GitOps for MLOps

The platform ensures seamless collaboration between data scientists, ML engineers, and developers��without the overhead of managing infrastructure.

📘 Course Overview: What You’ll Learn in AI268

AI268 focuses on equipping learners with hands-on skills in designing, developing, and deploying AI/ML workloads on Red Hat OpenShift AI. Here’s a quick snapshot of the course outcomes:

✅ 1. Explore OpenShift AI Components

Understand the ecosystem—JupyterHub, Pipelines, Model Serving, GPU support, and the OperatorHub.

✅ 2. Data Science Workspaces

Set up and manage development environments using Jupyter notebooks integrated with OpenShift’s security and scalability features.

✅ 3. Training and Managing Models

Use libraries like PyTorch or Scikit-learn to train models. Learn to leverage pipelines for versioning and reproducibility.

✅ 4. MLOps Integration

Implement CI/CD for ML using OpenShift Pipelines and GitOps to manage lifecycle workflows across environments.

✅ 5. Model Deployment and Inference

Serve models using tools like KServe, automate inference pipelines, and monitor performance in real-time.

🧠 Why Take This Course?

Whether you're a data scientist looking to deploy models into production or a developer aiming to integrate AI into your apps, AI268 bridges the gap between experimentation and scalable delivery. The course is ideal for:

Data Scientists exploring enterprise deployment techniques

DevOps/MLOps Engineers automating AI pipelines

Developers integrating ML models into cloud-native applications

Architects designing AI-first enterprise solutions

🎯 Final Thoughts

AI/ML is no longer confined to research labs—it’s at the core of digital transformation across sectors. With Red Hat OpenShift AI, you get an enterprise-ready MLOps platform that lets you go from notebook to production with confidence.

If you're looking to modernize your AI/ML strategy and unlock true operational value, AI268 is your launchpad.

👉 Ready to build and deploy smarter, faster, and at scale? Join the AI268 course and start your journey into Enterprise AI with Red Hat OpenShift.

For more details www.hawkstack.com 

[aisoftwaretesting]

Hybrid Testing: Combining Manual and Automated Testing

In the rapidly evolving field of software development, testing plays a vital role in ensuring product quality, performance, and reliability. While automated testing has gained significant traction due to its speed and scalability, manual testing remains essential in areas where human judgment and intuition are indispensable. This intersection has led to the rise of hybrid testing — a pragmatic and flexible approach that combines both manual and automated testing techniques to maximize the strengths of each.

In this article, we’ll explore the concept of hybrid testing, why it’s becoming the go-to strategy for many QA teams, and how platforms like Genqe enable organizations to implement hybrid testing effectively.

What is Hybrid Testing?

Hybrid testing refers to a software testing methodology that integrates both manual testing and automated testing techniques within a unified framework. This approach is not limited to any specific type of software or application; rather, it adapts to the unique demands of each project. Hybrid testing acknowledges that not all aspects of software testing can be efficiently automated and that human testers bring invaluable insight, especially in exploratory and usability testing.

This method allows teams to leverage automation for repetitive and time-consuming tasks while still relying on manual testing for tasks that require human perception, domain knowledge, and emotional intelligence.

Why Hybrid Testing?

Pure manual or pure automation approaches often fall short in real-world testing scenarios. Here’s why hybrid testing is essential:

Balance of Speed and Quality: Automated testing accelerates regression cycles, while manual testing ensures comprehensive exploratory testing. Hybrid testing balances both.

Cost-Efficiency: Automation can be expensive initially but pays off over time. Hybrid testing optimizes costs by automating only where it adds real value.

Scalability: Projects evolve, and so do their testing needs. A hybrid approach can easily adapt to increasing complexity or changing requirements.

Error Detection: Automated tests may miss certain visual or UX issues that manual testers can catch.

Agility and Flexibility: Modern Agile and DevOps environments benefit from hybrid testing’s adaptability.

Types of Hybrid Models in Software Testing

Hybrid testing isn’t one-size-fits-all. It comes in several models tailored to different workflows:

Layered Hybrid Model: Manual testing for the UI layer, automation for backend and APIs.

Parallel Hybrid Model: Manual and automated tests run concurrently for faster feedback.

Sequential Hybrid Model: Manual tests are executed first, followed by automation, or vice versa.

Modular Hybrid Model: The application is broken into modules, with some fully automated and others tested manually.

Data-Driven Hybrid Model: Combines manual test scenarios with data-driven automation to validate edge cases efficiently.

Tools like Genqe help teams structure these hybrid models intelligently by using AI-driven automation alongside human-guided testing paths.

When to Use Manual Testing

Manual testing continues to hold importance in the QA lifecycle. It is most effective in the following scenarios:

Exploratory Testing: Ideal for early development phases or when testing new features.

Usability Testing: Requires human interaction to assess user experience.

Visual and UI Testing: Colors, font alignment, spacing — these are best judged by the human eye.

Short-term Projects: Where the overhead of automation isn’t justified.

One-off Test Cases: That aren’t likely to be reused or repeated.

In such contexts, relying solely on automation could lead to missed bugs or user experience issues. Hybrid testing ensures these manual processes are not ignored.

When to Use Automated Testing

Automation excels in scenarios that demand repeatability, speed, and scale. It’s most effective in:

Regression Testing: Repetitive testing of existing features after updates.

Load and Performance Testing: Simulating thousands of users interacting with the system.

Data-driven Testing: Testing with multiple data sets.

Unit Testing and API Testing: Fast and precise verification of business logic and integrations.

Cross-browser/Cross-device Testing: Ensuring consistency across platforms.

Platforms like Genqe allow testers to automate these test types with minimal scripting by leveraging natural language and intelligent self-healing features.

Key Benefits of Hybrid Testing

Increased Coverage: Combines the strengths of human insight and machine precision.

Faster Time-to-Market: Automation speeds up repetitive tests; manual testing ensures quality in sensitive areas.

Optimized Resource Utilization: Manual testers and automation engineers can work in tandem.

Risk Mitigation: Better detection of critical bugs, especially in UX or real-world usage.

Improved ROI: Automate high-value areas and test the rest manually — the best of both worlds.

With Genqe, hybrid testing is even more seamless. Its intelligent AI agents identify what should be automated and provide suggestions to optimize manual efforts, ensuring your team gets the highest value with minimal duplication.

Strategies for Implementing Hybrid Testing

To implement hybrid testing successfully, teams must consider the following strategic steps:

Assess Testing Requirements: Classify test cases into what should be automated and what must be manual.

Select the Right Tools: Choose automation tools like Genqe that support both manual workflows and advanced automation.

Build a Reusable Framework: Create modular, maintainable test components that can be reused across test cases.

Integrate CI/CD Pipelines: Ensure automated tests are part of continuous integration workflows, while manual test cycles are scheduled strategically.

Monitor and Iterate: Regularly analyze test effectiveness and evolve the strategy as the product grows.

Foster Collaboration: Encourage open communication between manual testers and automation engineers for maximum synergy.

Genqe stands out here by providing a collaborative testing platform where both manual and automated tests can be written in plain English, managed together, and executed in a unified dashboard.

Challenges in Hybrid Testing

Despite its benefits, hybrid testing has its challenges:

Complexity in Test Management: Balancing and managing two test streams requires solid coordination.

Tool Integration Issues: Not all tools support hybrid approaches out of the box.

Maintenance Overhead: Automated scripts require maintenance, and manual tests need documentation.

Skill Gaps: Testers may need cross-functional skills in both manual and automation practices.

Test Redundancy: Risk of overlapping tests when not clearly defined.

However, tools like Genqe mitigate many of these challenges with intelligent planning, AI recommendations, and unified dashboards for managing both manual and automated test efforts cohesively.

Conclusion

In today’s fast-paced development environments, rigid testing methodologies no longer suffice. Hybrid testing presents a balanced, adaptive, and highly effective approach to quality assurance by merging the intuition of manual testing with the efficiency of automation. It acknowledges that no single strategy is perfect, and the optimal path lies in combination.

By embracing hybrid testing and using intelligent platforms like Genqe, organizations can ensure high-quality releases, faster time-to-market, and a more resilient QA process. As software complexity increases and user expectations grow, the future of testing undoubtedly lies in hybrid models — flexible, intelligent, and inclusive of both machine precision and human creativity.

[public-cloud-computing]

Uncover the evolution of DevOps with AI's transformative touch. Dive deep into a comprehensive analysis, unlocking the future of agile infrastructure. Engage with innovation in every byte!

[rubylogan15]

Unlock the future of DevOps with our in-depth analysis on AI's transformative impact. Dive into the synergy reshaping infrastructure. Revolutionize your approach now!

[paulcook159-blog]

Explore the transformative impact of AI on DevOps infrastructure. From efficiency gains to enhanced security, discover the key facets reshaping the future of software development.

[dieterziegler159]

Explore the transformative impact of AI on DevOps infrastructure. From efficiency gains to enhanced security, discover the key facets reshaping the future of software development.

[enterprise-cloud-services]

Explore the transformative impact of AI on DevOps infrastructure. From efficiency gains to enhanced security, discover the key facets reshaping the future of software development.

[atplblog]

Price: [price_with_discount] (as of [price_update_date] - Details) [ad_1] Elevating Software Testing with Artificial Intelligence By Vinaysimha Varma Yadavali The landscape of software testing is evolving, and Artificial Intelligence (AI) is at the forefront of this transformation. In today's fast-paced development environment, traditional testing methods struggle to keep up with increasing complexity, shorter release cycles, and growing customer expectations. This book provides a comprehensive guide to leveraging AI-driven techniques for next-generation software testing, helping you enhance efficiency, accuracy, and scalability. Why This Book? Whether you're a tester, developer, QA engineer, or tech leader, this book bridges the gap between software testing fundamentals and AI-powered innovations, offering: ✔ In-depth theoretical concepts ✔ Practical AI-driven testing strategies ✔ Real-world case studies and examples What You’ll Learn:Core Software Testing Principles – Explore manual vs. automated testing, STLC, and modern testing challenges.Introduction to AI & Machine Learning – Understand how AI models work and their applications in software testing.AI-Powered Test Automation – Learn how AI enhances test case generation, self-healing automation, and predictive analytics.Real-World AI Testing Applications – Apply AI to performance testing, security testing, and CI/CD pipelines.Popular AI-Driven Testing Tools – Hands-on insights into Selenium, Appium, Cypress, JMeter, and AI-powered automation frameworks.The Future of AI in Testing – Discover emerging trends, ethical challenges, and career opportunities in AI-powered QA. 👥 Who Should Read This Book? 🔹 Software Testers & QA Engineers – Upgrade your skills with AI-powered automation. 🔹 Developers & DevOps Professionals – Learn to integrate AI testing in CI/CD workflows. 🔹 Tech Leaders & Managers – Improve software quality using AI-driven strategies. 🔹 AI & ML Enthusiasts – Understand how AI transforms testing in modern software engineering. 🔥 What Makes This Book Unique? ⭐ Easy-to-understand AI concepts for testers & QA professionals ⭐ Industry-relevant tools and strategies for AI-driven testing ⭐ Insights from a seasoned Selenium contributor About the Author Vinay Varma is a leading expert in software testing, AI-powered automation, and an active contributor to the Selenium open-source community. With two decades of experience in test automation, AI in testing, and software quality assurance, Vinay has played a key role in advancing modern testing practices. As a Selenium contributor, he has actively worked on improving test automation frameworks, documenting best practices, and supporting the global testing community. His deep expertise in AI-driven automation, DevOps, and continuous testing has helped organizations streamline their software testing strategies and adopt cutting-edge technologies. Beyond his Selenium contributions, Vinay has served as a judge and review panelist for research papers, evaluating innovations in AI, automation, and software quality engineering. His thought leadership continues to shape the future of AI-powered software testing. Elevate Your Software Testing Skills with AI! If you're ready to leverage AI for smarter, faster, and more reliable software testing, this book is your ultimate guide. Available in Kindle eBook & Paperback. Get your copy today! ASIN ‏ : ‎ B0DVNS8K3D Language ‏ : ‎ English File size ‏ : ‎ 1.1 MB Simultaneous device usage ‏

: ‎ Unlimited Text-to-Speech ‏ : ‎ Enabled Screen Reader ‏ : ‎ Supported Enhanced typesetting ‏ : ‎ Enabled X-Ray ‏ : ‎ Not Enabled Word Wise ‏ : ‎ Not Enabled Print length ‏ : ‎ 280 pages [ad_2]