This post covers most content from the “Process and Technical Debt” lecture of our Machine Learning in Production course. It expands on discussions from an earlier blog post. For other chapters see the table of contents. Production systems with machine-learned components are usually built by interdisciplinary teams involving data scientists…

This chapter covers content from the “Infrastructure Quality, Deployment, and Operations” lecture of our Machine Learning in Production course. For other chapters see the table of content. The term operations describes all tasks, infrastructure, and processes involved in deploying, running, and updating a software system and the hardware it runs…

This chapter covers content from the “Managing and Processing Large Datasets” lecture of our Machine Learning in Production course. For other chapters see the table of content. Many systems exceed the resources provided by a single machine and may need to be scaled with changing demands. Most insights into how…

This chapter discusses automating some or all steps of ML pipelines in production projects with machine learning to make it easier to update models and experiment. For other chapters see the table of content. After discussing how and where to deploy a model, we now focus our attention on the…

This chapter covers content from the “Software Architecture of AI-enabled Systems” lecture of our Machine Learning in Production course. It focuses on deployment of a machine-learned model for inference tasks within a system, for the pipeline for training the model see the next chapter Automating the ML Pipeline, for scaling…

This chapter corresponds to the “Tradeoffs among Modeling Techniques” lecture of our Machine Learning in Production course. For other chapters see the table of content. Traditionally, data science education and data scientists in practice focus on prediction accuracy as the primary goal during model development. However, when used in a…

This chapter covers content from the “Software Architecture of ML-enabled Systems” lecture of our Machine Learning in Production course. For other chapters see the table of content. Investing in some planning before diving into implementation details helps to build systems that better meet requirements, especially when it comes to qualities…

This chapter introduces and organizes the various considerations that go into the design of ML-enabled systems and all their components. …

This chapter covers content from the “Interpretability and Explainability” lecture of our Machine Learning in Production course. For other chapters see the table of content. The final areas of concerns in responsible AI engineering that we discuss relate to transparency and accountability, both often important to enable users to build…