MLOps Hands-On Tutorial for Modern DevOps and ML Engineers
Introduction: Problem, Context & Outcome Machine learning initiatives deliver impressive results during experimentation; however, serious challenges appear when those models are pushed into production. In real organizations, models often fail due to unstable data pipelines, manual deployments, missing monitoring, and unclear ownership between data science and DevOps teams. Consequently, incidents increase, fixes become reactive, and … Read more