Practitioners Guide to MLOps: A framework for continuous delivery and automation of machine learning

This whitepaper provides an overview of the MLOps life cycle, MLOps processes, and capabilities and why they’re important for successful adoption of ML-based systems. It also deep dives into concrete details of running a continuous training pipeline, deploying a model, and monitoring predictive performance of ML models.

Front page of Practitioners Guide to MLOps report: A framework for continuous delivery and automation of machine learning

In this report, you will learn:

  • The MLOps life cycle and important processes and capabilities for successful ML-based systems
  • Orchestrating and automating the execution of continuous training pipelines
  • Model deployment and prediction serving
  • Dataset and feature management
  • Model management and governance

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