⚙️ DevOps / MLOps / AIOps [13-Jan-2026]

 

DevOps / MLOps / AIOps

AIOps & Monitoring

• What is AIOps? Guide to AI in IT Operations (2026)
  AIOps (AI for IT Operations) represents a fundamental shift in enterprise IT infrastructure management, combining AI capabilities with operational workflows to enable smarter automation, faster incident response, and predictive maintenance.
  Source: IR.com
• Fusing Chaos Engineering with AI Anomaly Detection
  Modern DevOps teams are combining chaos engineering principles with AI-powered anomaly detection to build resilient systems with improved Mean Time To Resolution (MTTR) by proactively identifying and fixing issues before they impact users.
  Source: CloudNativeNow

MLOps & Model Management

• Building an End-to-End MLOps Platform on AWS (Part 1)
  A comprehensive guide on architecting MLOps platforms on AWS, covering model training pipelines, deployment strategies, and monitoring practices to ensure production-ready ML systems with automated workflows.
  Source: Medium

Container & Orchestration

• Designing a Scalable Multi-Cloud DevOps Architecture
  The future of multi-cloud DevOps architecture is defined by AI-powered AIOps, GitOps automation, and self-healing systems. Organizations are moving beyond single-cloud strategies to leverage best-of-breed services across cloud providers.
  Source: NareshIT
• CI/CD Pipeline Failures in Container-Based Deployments
  Common causes of containerized CI/CD pipeline failures include image issues, config drift, and resource limits. The article provides comprehensive prevention strategies and best practices for building reliable deployment pipelines.
  Source: C# Corner

Infrastructure as Code

• Governance as Code for DevOps: A Practical Guide
  Governance as Code applies infrastructure-as-code principles to compliance and policy management, integrating security and governance directly into CI/CD pipelines to ensure every deployment meets organizational standards.
  Source: Spacelift