DevOps / MLOps / AIOps
DevOps Tools & Platforms
• From DevOps to MLOps (A Practical Guide for 2026) - Guide helping DevOps engineers leverage existing skill sets to upskill in MLOps, bridging the gap between infrastructure and machine learning operations. Source: DevOpsCube
• How AI Will Redefine DevOps in 2026 - As AI accelerates code and test creation, pipelines must become autonomy-ready and self-scaling to handle unexpected workloads. Source: DevOps Digest
• 12 Must-Have Skills for DevOps Engineers in 2026 - Essential skills including advanced Kubernetes orchestration, Infrastructure as Code with Terraform, and GitOps practices. Source: Medium
MLOps Platforms
• Why DevOps Mental Models Fail for MLOps in Production AI - Analysis of MLOps challenges in production AI environments and practical solutions for teams building AI development services. Source: Linear Loop
• MLOPs vs DevOps – Understanding the Difference - MLOps explained as the operational framework built specifically for machine-learning workflows, expanding DevOps principles. Source: NASSCOM
AIOps & Intelligent Operations
• New Relic AI Impact Report 2026: How AIOps is Solving Real Problems - Report showing how AIOps and intelligent observability transform incident response from reactive emergencies to proactive exercises. Source: New Relic
• The 2026 IT Leader's Priority Shift: Why AI, Resilience, and Unified Visibility Matter - IT leaders prioritizing AI readiness, operational resilience, and unified visibility to maintain complex system reliability at scale. Source: LogicMonitor
• 3 E Network Launches Intellisight™ Platform for AI Compute Infrastructure with AIOps - New Intellisight platform combining AIOps and active security for AI compute infrastructure management. Source: Globe Newswire
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