Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning. White paper May 2021 Authors: Khalid Salama, Jarek Kazmierczak, Donna SchutTable of Contents Executive summary 3 Overview of MLOps lifecycle and core capabilities 4 Deep dive of MLOps processes 15 Putting it all together 34 Additional resources 36 Building an ML-enabled system 6 The MLOps lifecycle 7 MLOps: An end-to-end workflow 8 MLOps capabilities 9 Experimentation 11 Data processing 11 Model training 11 Model evaluation 12 Model serving 12 Online experimentation 13 Model monitoring 13 ML pipelines 13 Model registry 14 Dataset and feature repository 14 ML metadata and artifact tracking 15 ML development 16 Training

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