Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning by Google Cloud

Across industries, DevOps and DataOps have been widely adopted as methodologies to improve quality and reduce the time to market of software engineering and data engineering initiatives. With the rapid growth in machine learning (ML) systems, similar approaches need to be developed in the context of ML engineering, which handle the unique complexities of the practical applications of ML. This is the domain of MLOps. MLOps is a set of standardized processes and technology capabilities for building, deploying, and operationalizing ML systems rapidly and reliably.

The document is in two parts. The first part, an overview of the MLOps lifecycle, is for all readers. It introduces MLOps processes and capabilities and why they’re important for successful adoption of ML-based systems.

The second part is a deep dive on the MLOps processes and capabilities. This part is for readers who want to understand the concrete details of tasks like running a continuous training pipeline, deploying a model, and monitoring predictive performance of an ML model.

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