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MLOps: The Infrastructure Behind Production AI
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Machine Learning Operations (MLOps)
Operationalizing and Scaling Machine Learning
Infrastructure and processes for deploying, monitoring, and managing ML models in production. Includes model versioning, deployment pipelines, monitoring, retraining automation, ML infrastructure, and model governance. Maturity progresses from manual, one-off deployments to automated, scalable ML systems with continuous improvement.
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MLOps Infrastructure Is Becoming a Commodity (While Skills Remain Scarce)
Pragmatic MLOps approaches for organizations without dedicated platform teams or unlimited budgets. Focuses on managed services, open-source tools, and simplified workflows that provide core MLOps benefits—versioning, monitoring, retraining—without enterprise complexity.
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Most Machine Learning Models Die in Notebooks – The Industrial Revolution Explains Why
Examines why most ML models never make it to production and provides a systematic approach to bridging the deployment gap. Covers productionization requirements, the skills and infrastructure prerequisites often missing, and the handoff patterns between data scientists and engineers that enable successful deployment.
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