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Forbes
30-07-2025
- Business
- Forbes
DevOps In The Age Of AI: What's Hype, What's Real
Ian Amit, CEO & Co-Founder at Gomboc AI. Let's cut through the noise: AI in DevOps is everywhere, and the hype machine is running at full throttle. Every vendor claims their AI tool will make your engineers obsolete, automate away your headaches and maybe even make your coffee. But here's the reality: AI is moving fast, but it's not even close to replacing real engineers, and anyone telling you otherwise is overselling unproven capabilities. The Productivity Bump—But At What Cost? Sure, AI tools are making some workflows faster. Copilot, ChatGPT, you name it—they generate large volumes of code quickly, suggest fixes and automate the boring stuff. That's great. But let's not pretend this is magic. The real productivity boost comes when you pair these tools with experienced engineers who know when to trust the output and when to throw it in the trash. The uncomfortable truth is that most GenAI code tools still amount to "vibe coding." A recent study cited by CyberScoop found that AI-generated code from popular LLMs was riddled with security vulnerabilities, often passing insecure patterns as valid solutions. The result is low adoption and even lower trust. Engineers know better than to ship black-box code that could trigger outages or introduce critical flaws. Using AI In DevOps Without Breaking Things If you're going to bring AI into your DevOps workflow, do it with eyes wide open. The key is to treat AI like a junior engineer, fast, helpful, but absolutely not production-ready without review. Start with clear review protocols: No AI-generated code should hit production without human oversight. Set up mandatory peer reviews, static analysis and automated tests to catch the things AI can't see. And make sure your engineers know that the goal isn't to rubber-stamp AI output; it's to assess it critically. Train your team on what AI is good at and where it fails. Don't just drop a new tool into the stack and assume people will figure it out. Run internal sessions where teams compare AI suggestions to known best practices, especially for security-sensitive infrastructure or compliance-heavy environments. Set guardrails for usage. For example, using AI to scaffold boilerplate code or generate documentation? Great. Letting it modify Terraform policies that govern your cloud security posture without policy validation? Hard no. Bottom line: AI can augment your team, but it shouldn't be the final reviewer. Build systems that combine speed with safeguards, and you'll see the productivity gains without the slip-ups. Security: Still A Human Problem Let's talk about security. "Vibe coding" showed us what happens when you let AI loose without guardrails: code that looks plausible but is fundamentally broken. Recent research found that a number of AI-generated web apps had glaring vulnerabilities. If you're betting your business on AI catching everything, you're going to lose badly. Why AI Isn't Replacing Engineers (It's Just Another Tool) Here's the truth: AI is a force multiplier, not a replacement. It's a power tool, not an autopilot. The best teams use AI to automate the grunt work, but they don't let it make the calls. You still need engineers with domain expertise to review, reason and, frankly, challenge, review and/or verify AI-generated output. Blind trust is how you end up with breaches, outages and a lot of critical failures and reputational damage. And let's be clear: Most of today's models are still too generic. They're built to be everything to everyone, which often means they miss the mark on the domain-specific nuance that's critical in production environments. For example, we've seen large language models suggest default configurations for AWS resources without accounting for organizational policies or regional compliance constraints, something that would never pass an internal security review. Until these systems become both more specialized and reliably context-aware, they're not replacing engineers; they're just creating more review cycles. We can see more evidence of this from Apple's recent research paper, which says that while GenAI tools can handle basic reasoning tasks, their ability to reason falls apart as problems become more complex, suggesting that current claims about GenAI 'reasoning' should be treated with caution. What Engineering Teams Should Be Doing Now To integrate AI responsibly into DevOps workflows, leaders should adopt a structured and risk-aware approach. Start by asking foundational questions: • What role will AI play in your development lifecycle? • What types of code can it generate, and where must human review remain non-negotiable? • How will outputs be tested, validated and governed? Establish a review-first culture. Every AI-generated suggestion—whether it's code, a config or a remediation—should go through peer review and automated validation. Treat AI as an assistant, not an authority. Consider deterministic AI over generative AI in sensitive areas. Deterministic AI systems don't generate probabilistic guesses. Instead, they apply pre-defined logic, policies and rules to produce consistent, explainable outputs, which is especially useful for security-critical workflows like Infrastructure as Code remediation or compliance enforcement. Use a two-step evaluation model: • Contextual Accuracy: Does the AI understand and reflect the architecture, compliance boundaries and operational patterns of your environment? • Policy Alignment: Does the output meet internal guardrails, regulatory standards and business logic? The Bottom Line AI in DevOps is here to stay, but it's not the revolution you're being sold. It's a tool—sometimes a great one, sometimes a liability. If you want to move fast and not break things, use AI to boost your engineers, not replace them. Mature teams won't trust "vibe coding" to run production. They'll invest in AI that's precise, policy-aware and accountable. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Time Business News
26-07-2025
- Business
- Time Business News
Why DevNet Associate Training Is Ideal for Career Upskilling?
In today's fast-moving tech world, staying competitive requires constant upskilling. As businesses evolve, the demand for network automation, cloud solutions, and software-driven networking is skyrocketing. For professionals in traditional networking roles, learning new skills is no longer optional it's essential. Whether you're already in IT or just starting your career, Cisco's DevNet Associate certification is an ideal tool to take your career to the next level. This training dives into the world of network automation, APIs, cloud technologies, and programming, all of which are at the forefront of modern IT environments. It's perfect for anyonewhether you're a network engineer, a software developer, or someone eager to enter the tech field looking to stay relevant and boost your career. In this blog, we'll explore why DevNet Associate training is an excellent choice for career upskilling, how it aligns with the latest tech trends, and the many benefits it offers. We'll also spotlight the importance of hands-on practice and how platforms like PyNet Labs can help you prepare for the certification. Traditional networking involved manual configurations, complex command-line setups, and troubleshooting by trial and error. However, as businesses scale, the need for automation has become crucial. Tools like Python, Ansible, Terraform, and APIs are transforming how network engineers approach their work. Today, companies are automating network configurations, monitoring, and management leading to increased efficiency and reduced human error. Automation is not just a trend; it's the future. DevNet Associate training ensures you're equipped with the skills to thrive in this new world. Through hands-on labs and real-world scenarios, you will: Learn how to use Python and Ansible to automate network tasks. Work with APIs to configure and manage network devices. Explore cloud-based network management and automation platforms. This training is your gateway to mastering the tools and techniques that are now indispensable in the IT world. By gaining these skills, you'll stay ahead of industry trends and contribute meaningfully to your team's shift toward more automated and efficient systems. The world of IT is rapidly evolving with the adoption of cloud computing, software-defined networks (SDN), and microservices. As businesses embrace these technologies, the demand for professionals who can bridge the gap between software development and networking has surged. DevNet Associate covers a wide array of essential topics, including: Cloud computing platforms (AWS, Azure, Google Cloud). Software-defined networking (SDN) and network automation tools. API integrations and cloud service management. By gaining expertise in these areas, you position yourself to stay ahead of the curve. These technologies are central to the future of IT, and the DevNet Associate certification helps you align with the industry's current and future demands. DevNet Associate training places a strong emphasis on hands-on learning because while theory is important, experience is crucial. The best way to truly understand these technologies is by using them in real-world scenarios. Throughout the course, you will: Automate network configurations using Python scripts. Set up CI/CD pipelines for automating software and network management. Work directly with APIs to interact with networking devices and cloud services. This hands-on experience is invaluable. Whether you're managing a live network or solving a cloud-based coding challenge, you'll gain the confidence to apply your knowledge in real-world situations. Employers greatly value this practical expertise, as it shows that you can adapt quickly to emerging technologies and solve problems efficiently. To further enhance your learning experience, platforms like PyNet Labs provide a fantastic opportunity to sharpen your skills. PyNet Labs offers a wide range of practice labs designed to simulate real-world networking challenges, giving you the perfect environment to test your knowledge and gain hands-on experience. The demand for network automation, cloud computing, and programming skills has never been higher. According to the U.S. Bureau of Labor Statistics, IT and network management jobs are expected to grow by 8% over the next decade significantly faster than the average for other occupations. This means more opportunities for roles like network engineers, cloud engineers, and DevOps professionals. By earning your DevNet Associate certification, you'll open doors to various high-demand positions, including: Network Automation Engineer : Automate and optimize network systems to cut operational costs and boost efficiency. : Automate and optimize network systems to cut operational costs and boost efficiency. DevOps Engineer : Develop automated workflows for software development and deployment. : Develop automated workflows for software development and deployment. Cloud Engineer : Design, build, and manage cloud infrastructure. : Design, build, and manage cloud infrastructure. Software Developer: Create applications that integrate with network devices and manage network functions. These roles are not only highly compensated but also offer exceptional growth potential. By acquiring these skills, you're setting yourself up for long-term career success. One of the standout features of DevNet Associate training is that you don't need a background in programming to get started. Many professionals in networking and IT may not have coding experience, and Cisco designed the DevNet Associate program to be accessible to everyone even those new to programming. The course begins by introducing you to Python, one of the most widely used languages in network automation. As you progress, you'll dive into more advanced topics like APIs, cloud computing, and CI/CD pipelines. With the flexibility to learn at your own pace, you'll build confidence and grow your technical expertise step by step. By the end of the program, you'll be equipped with a solid foundation in both network engineering and software development skills that are in high demand across the tech industry. Once you've earned your DevNet Associate certification, you'll be well-positioned to pursue more advanced Cisco certifications, including: DevNet Professional (350-901 DCPA) : This takes a deeper dive into automation and cloud-native technologies. : This takes a deeper dive into automation and cloud-native technologies. CCNP (Cisco Certified Network Professional) : Focuses on enterprise networking, routing, switching, and security. : Focuses on enterprise networking, routing, switching, and security. CCIE (Cisco Certified Internetwork Expert): A prestigious certification for networking experts who want to demonstrate mastery in automation and advanced networking. DevNet Associate provides the foundational knowledge in automation and cloud technologies, preparing you for the challenges of advanced credentials. In today's rapidly evolving IT landscape, skills in network automation, cloud computing, and software development are crucial for career success. The DevNet Associate certification offers a fantastic opportunity to future-proof your career and gain the expertise needed to thrive in the modern tech world. Whether you're already in networking or breaking into IT, DevNet Associate training will equip you with practical, hands-on skills that are in high demand. Tools like PyNet Labs provide an excellent platform for practicing your new skills, ensuring you're ready to take on the certification exam and excel in your career. TIME BUSINESS NEWS
Time of India
25-07-2025
- Time of India
Harish Govinda Gowda as a novel force in global tech
As industries accelerate towards cloud-native ecosystems and AI-powered operations, the demand for infrastructure that is fast, fault-tolerant, and future-ready has never been higher. At the forefront of this transformation is Harish Govinda Gowda, a globally recognised leader in DevOps, Site Reliability Engineering (SRE), and secure cloud architecture. His work is reshaping how modern enterprises approach infrastructure — not just as a technology layer, but as a living, responsive framework driving continuous innovation. Throughout his career, Harish has been instrumental in leading end-to-end modernisation initiatives across diverse industries. From orchestrating multi-region cloud migrations to architecting resilient Kubernetes clusters, his solutions are built with resilience, automation, and agility at their core. Whether deploying high-traffic platforms like Adobe Experience Manager or optimising commerce platforms on EKS and GKE, his infrastructure blueprints consistently deliver both performance and precision. Harish's technical expertise spans a powerful stack of tools and platforms, including Terraform, GitOps (ArgoCD), Helm, Vault, SonarQube, and Prometheus. With these, he has built intelligent CI/CD pipelines, encrypted service meshes, and observability systems that monitor and heal infrastructure in real time. His work enables organisations to shift from reactive troubleshooting to proactive, automated operations — a critical evolution in today's always-on digital economy. His thought leadership is not limited to deployment engineering. Through a series of widely referenced publications, Harish has explored themes such as SLO-centric monitoring, automated remediation, runbook design, and predictive failure detection. His papers are now considered essential reading for DevOps professionals and SREs aiming to scale operations without sacrificing control or security. Topics like secure Helm-based deployments, policy enforcement across AWS and GCP, and incident response automation are central to his contribution to enterprise-grade reliability. A defining characteristic of Harish's philosophy is his commitment to 'resilience by architecture.' For him, infrastructure is more than a set of components — it is a dynamic organism capable of learning from stress and recovering without human intervention. His approach to self-healing systems, intelligent node patching, and AI-assisted incident recovery is setting new standards for what enterprise environments can and should achieve. Beyond the codebase, Harish remains a highly respected voice in global tech circles. He regularly serves as a judge and advisor at international engineering conferences, helping shape the standards for cloud operations, DevSecOps, and AI-integrated infrastructure. His academic engagements and mentorship roles have helped develop the next generation of infrastructure engineers who now carry forward his principles of automation, observability, and secure scalability. His ongoing initiatives in zero-trust architecture, multi-tenant governance, and cloud cost optimisation have delivered measurable business value — from reduced downtime and faster deployment cycles to improved compliance and resource efficiency. These are not just technical wins — they are strategic enablers for enterprises navigating the complexity of digital transformation. At a time when operational intelligence is as vital as business intelligence, Harish Govinda Gowda's work stands as a blueprint for next-generation infrastructure — deeply secure, self-aware, and designed to scale with intention. He doesn't just build systems that survive disruption; he builds systems that learn from it, adapt to it, and evolve through it.
India.com
16-07-2025
- Business
- India.com
Engineering Transformation Through Applied Innovation: How Gnanendra M Reddy Advances DevOps, AI, and Cloud-Native Compliance
In the evolving world of enterprise-scale cloud computing and DevOps transformation, few professionals exhibit the same practical command and foresight as Gnanendra M Reddy. With a career grounded in cloud migration, containerization, CI/CD automation, Cyber Securityand governance frameworks, Gnanendra's contributions address the critical needs for scalability, compliance, and operational efficiency. His expertise combines hands-on technical insight with strategic system design, a balance that sets him apart as a thought leader in cloud-native and DevOps spaces. Gnanendra's research explores key themes in DevOps enablement and cloud-native modernization. His work bridges production-level insights with sustainable system models, creating tested patterns for infrastructure modernization, all aligned with domain-specific needs and system behaviour. Enabling CI/CD Efficiency in R&D-Driven DevOps Environments Published in the Essex Journal of AI Ethics and Responsible Innovation, vol. 3, 2023, Gnanendra's paper, 'Agile and DevOps Transformation in Large-Scale R&D Centres: A Case Study on CI/CD Efficiency Gains', explores the operational challenges faced by R&D organizations. The study addresses deployment bottlenecks in environments with high experimental churn and asynchronous delivery timelines. By applying his expertise in Terraform, GitLab automation, and Kubernetes orchestration, Gnanendra designed modular CI/CD blueprints that supported isolated experimentation without compromising pipeline stability. 'By introducing controlled stages and approval checkpoints based on artifact type and criticality, we preserved delivery agility while enhancing governance,' Gnanendra states in the paper. His solution unified delivery rhythms across teams, maintaining research integrity while incorporating compliance checkpoints, reusable templates, and container scanning stages—all aligned with pipeline maturity and integration frequency. His work demonstrates the ability to scale DevOps philosophy without rigidity, merging innovation with structured automation. Automating Scalable Data Engineering with AI in Cloud Ecosystems In The Newark Journal of Human-Centric AI and Robotics Interaction, vol. 3, pp. 182–223, 2023, Gnanendra co-authored 'AI-Powered Data Engineering: Automating ETL Pipelines for Scalable Cloud Analytics'. The paper discusses how AI can optimize and automate ETL processes across complex cloud environments. Addressing challenges such as diverse datasets and fluctuating data volumes, Gnanendra designed a dynamic orchestration framework combining pipeline inference, metadata tagging, and adaptive scheduling. 'Automation in data engineering must respect the fluidity of data behaviour while maintaining consistency in lineage and validation,' Gnanendra explains in the article. His solution used reinforcement logic to determine pipeline run triggers, reducing idle cycles and improving throughput. The AI-based classifiers predicted data readiness based on historical trends, ensuring timely processing. The system's strength lies in its dynamic Directed Acyclic Graph (DAG) optimization, customized for domain-specific workload patterns. This work reflects Gnanendra's ability to integrate AI into orchestration, optimizing both data engineering constraints and intelligent pipeline mechanics. His approach resulted in measurable improvements in data readiness and orchestration efficiency. Securing Cloud-Native Compliance Through Containerized Migration In the American Journal of Data Science and Artificial Intelligence Innovations, vol. 2, pp. 147–186, 2022, Gnanendra's research titled 'Ensuring Compliance in Cloud-Native Deployments: Migrating VMware Tanzu Workloads to Azure Kubernetes Service (AKS)' addresses compliance enforcement during containerized workload migrations. Focused on large-scale migration projects, his work strikes a balance between achieving cloud-native scalability and maintaining regulatory traceability. 'Ensuring traceable compliance must begin at the deployment plan, not post-deployment,' Gnanendra emphasizes. His research outlines a layered migration process where workloads are containerized and migrated in waves based on classification and dependency mapping. By integrating policy-as-code modules into Kubernetes deployment processes, Gnanendra ensured both runtime security validation and post-deployment compliance reporting. The paper also highlights his use of GitOps workflows for controlled change propagation and audit logging. This architecture demonstrates his mastery of cloud security, operational scale, and compliance fidelity, solidifying Gnanendra's capability to lead secure and scalable migration efforts. Certifications: Deep Expertise in Cloud-Native and DevSecOps Domains Gnanendra's certifications underscore his expertise in cloud-native technologies, automation frameworks, and security compliance. At the core of his portfolio is his recognition as a Kubestronaut, a title earned through his advanced capabilities in Kubernetes architecture, security, and multi-cluster orchestration. This distinction sets him apart as an industry leader in orchestrating containerized applications at scale. His Kubernetes certifications include: Certified Kubernetes Security Specialist (CKS) Certified Kubernetes Administrator (CKA) Certified Kubernetes Developer (CKAD) Kubernetes and Cloud Native Associate (KCNA) Kubernetes and Cloud Security Associate (KCSA) Additionally, Gnanendra holds the HashiCorp Terraform Certified Associate (003) certification, demonstrating his ability to manage infrastructure as code across multi-cloud environments. Complementing his DevOps expertise, Gnanendra has earned credentials in compliance and platform governance: Certified CIS – Risk and Compliance Implementer (ServiceNow) Certified System Administrator – ServiceNow ITIL V3 Certified, confirming his expertise in structured service management across IT operations. These certifications strengthen Gnanendra's ability to lead end-to-end transformation initiatives. His qualifications cover everything from design and automation to security and compliance, ensuring comprehensive, scalable solutions. Transforming Challenges into Scalable Solutions Gnanendra's contributions, reflected in his research and certifications, showcase his ongoing ability to address enterprise-scale challenges and deliver secure, scalable solutions. His work combines domain expertise with an execution-first mindset, continuously shaping infrastructure strategies that are practical, reproducible, and ready for regulation. As cloud-native ecosystems evolve, Gnanendra's decisions, governance models, and automation frameworks remain relevant, impactful, and aligned with the demands of high-compliance, high-performance environments.
Hans India
11-06-2025
- Business
- Hans India
The serverless shift: Scaling smarter with fewer resources and no infrastructure bloat
In today's fast-paced digital era, agility, scalability, and efficiency have become critical imperatives rather than luxuries. Serverless computing, which eliminates infrastructure management and allows developers to focus purely on delivering value, is emerging as a powerful response to these demands. At the forefront of this transformation is Anil Kumar Manukonda, a cloud innovator who has been instrumental in architecting high-performance, cost-effective solutions for modern enterprises. 'Serverless is more than a trend—it's a mindset shift,' says Anil. 'It empowers teams to build smarter, faster, and with fewer operational burdens. The true value lies in accelerating innovation without compromising reliability or security.' Anil's pioneering work includes the design of a mission-critical, event-driven data ingestion pipeline using AWS Lambda, Kinesis Data Streams, and DynamoDB. The system now processes over 200,000 events per hour with sub-250ms latency—a dramatic improvement over the EC2-based batch system it replaced. 'We achieved a 50% reduction in processing time while completely eliminating downtime,' he explains. By implementing provisioned concurrency, cold-start latency dropped by 90%, ensuring consistent performance even during peak traffic. His leadership extended to transforming a legacy reporting microservice into a serverless REST API with API Gateway, Lambda, and RDS Proxy. The result? A 300% increase in throughput and $120,000 in annual cloud savings. Anil also introduced a centralized governance framework using Terraform modules and AWS Config Rules, enabling consistent, compliant deployments across five critical business applications. The impacts are measurable: 30% higher developer productivity, 80% fewer budget overruns, and a 40% cut in incident response time—thanks in part to automation using SNS and DynamoDB streams. One of his most ambitious projects involved rearchitecting an order processing system with SQS and Lambda, scaling it to 250,000 orders per day and reducing fulfillment times from five minutes to just 30 seconds. 'Serverless lets you scale from zero to massive workloads with grace,' Anil notes. 'But success also means solving new challenges—latency, state management, and security.' His solutions include hybrid warming strategies and IAM automation across multi-account AWS environments. Anil's contributions extend beyond the workplace. He's the author of influential publications like Designing Highly Available and Scalable Web Applications Using Azure Functions and Automating Infrastructure Provisioning Using Terraform. He sees the future of cloud computing in AI-driven serverless functions, edge-native services, and function mesh architectures. 'Serverless is no longer just about cost savings,' he emphasizes. 'It's about enabling resilient, intelligent, and scalable architectures that redefine what's possible.' Through vision, execution, and thought leadership, Anil Kumar Manukonda is not just navigating the serverless frontier—he's charting its course.
