Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. Today we’ll demonstrate how a well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team. We identified design patterns and code that we could leverage. We brought together over 30+ resources from various countries and functions including the Azure ML Product and Engineering team to align the efforts and develop the MLOps v2 accelerator, aligning with the development of Azure Machine Learning v2 platform, CLI, and SDK. The result is we now have a codebase that contains repeatable, automated, and collaborative workflows and patterns that include the best practices for deploying machine learning models to production.
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