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Engineering Practices for Data Scientists

This eBook will help you pick up engineering best practices with simple tips.

Engineering Practices for Data Scientists

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Software engineering has come a long way. It’s no longer just about getting a functioning piece of code on a floppy disk; it’s about the craft of making software. There’s a good reason for it too. Code lives for a long time.

Thus there are a lot of strong opinions about good engineering practices that make developing software for the long haul possible and more enjoyable. I think enjoyability is an important word here because most software developers know the pain of fixing poorly developed and poorly documented legacy software.

Data scientists are also entering this world because machine learning is becoming a core part of many products. While a heterogenous bunch with various backgrounds, data scientists are more commonly from academia and research than software engineering. The slog of building and maintaining software isn’t as familiar as it is to most developers, but it will be soon enough. It’s better to be prepared with a solid foundation of best practices, so it’ll be easier to work with software engineers, and it’ll be easier to maintain what you build.

This eBook is to help pick up engineering best practices with simple tips. I hope that we can teach even the most seasoned pros something new and get you talking with your team on how you should be building things. Remember, as machine learning becomes a part of software products, it too will live for a long time.

This eBook isn’t about Valohai – although there is a section about our MLOps platform at the end – but good engineering is close to our heart.

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