Deploying machine learning (ML) solutions in production introduces many challenges that don't arise in standard software development projects. ML solutions are more complex and trickier to get right in the first place. They also exist in usually volatile environments, where the data distribution deviates significantly over time for a variety of expected and unexpected reasons.

These issues are further aggravated by the fact that many ML practitioners don't come from a software engineering background, so they might not be familiar with the best practices of this industry, such as writing testable code, modularizing components, and using version control effectively. These challenges create technical debt, and solutions become more complex and difficult to maintain over time, powered by a compounding effect, for ML teams.

This guide enumerates ML operations (MLOps) best practices that help mitigate these challenges in ML projects and workloads.

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