Klaviyo Data Science Podcast EP 38 | Production 101

Klaviyo Data Science Podcast

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Welcome back to the Klaviyo Data Science podcast! This episode, we dive into…

An introduction to production

What comes after you finish building a data science model? If you’re working on a software project, the answer likely involves that model serving customers in production. Understanding production is crucial for any data scientist or software engineer, so we spend this episode learning about best practices from three experienced Klaviyo engineers.

Listen along to learn more about:

How to make sure your code is “battle-ready,” whether you’re working on a data science project or not
Why error messages you think are safe to ignore may not actually be safe to ignore
One key lesson for safely deploying your code, no matter what environment you work in

“That’s stuck with me through the years: there are these knock-on effects between things. Even if it’s not your code, you should still try to understand how it’s working and whether it can have a ripple effect that comes back and affects your code.”— Chris Conlon, Lead Software Engineer

Check out the full show notes on Medium!

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