Introduction: Problem, Context & Outcome Today, engineering teams in Bangalore manage fast-growing infrastructure across cloud, containers, and hybrid platforms. However, many engineers still rely on manual configurations or fragmented scripts. As a result, they face frequent configuration drift, inconsistent deployments, and delayed releases. Moreover, learning Ansible through self-study often leads to shallow knowledge that fails … Read more
Introduction: Problem, Context & Outcome Modern engineering teams must release software quickly; however, they must also keep systems reliable, secure, and available at all times. Unfortunately, many teams still struggle with outages, alert fatigue, unclear incident ownership, and unstable deployments. As organizations adopt cloud platforms, microservices, and CI/CD pipelines, complexity increases rapidly. Therefore, traditional operations … Read more
Introduction: Problem, Context & Outcome Today’s software systems are expected to be fast, always available, and scalable under unpredictable demand. Engineering teams struggle with service outages, unstable releases, excessive alerts, and unclear operational ownership. As architectures move toward cloud-native and microservices, traditional operations models fail to keep up. Simply adding tools or manpower no longer … Read more
Introduction: Problem, Context & Outcome Engineering teams today run applications on container platforms that demand constant availability, security, and scale. Kubernetes clusters grow quickly, workloads change frequently, and delivery pipelines move faster than ever. Without strong platform administration skills, teams experience unstable deployments, security gaps, inefficient resource usage, and slow incident recovery. Manual interventions increase … Read more
Introduction: Problem, Context & Outcome Infrastructure teams still spend a significant amount of time performing repetitive manual tasks. Server provisioning, application deployment, configuration updates, and patch management often depend on human intervention. As environments grow across cloud, on-premise, and hybrid platforms, these manual processes introduce delays, inconsistencies, and failures. Even skilled engineers struggle to maintain … Read more
MLOps Foundation Certification—A Practical Blueprint for Production-Grade Machine Learning in DevOps Teams Introduction: Problem, Context & Outcome Many organizations succeed at building machine learning models but fail at running them in production. Teams deliver strong experiments, yet deployments break under real traffic and changing data. Data scientists push updates without operational visibility, while DevOps teams … Read more