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Content provided by Klaviyo Data Science Team. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Klaviyo Data Science Team or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
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Klaviyo Data Science Podcast EP 38 | Production 101

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Manage episode 373806168 series 3251385
Content provided by Klaviyo Data Science Team. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Klaviyo Data Science Team or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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!

  continue reading

58 episodes

Artwork
iconShare
 
Manage episode 373806168 series 3251385
Content provided by Klaviyo Data Science Team. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Klaviyo Data Science Team or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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!

  continue reading

58 episodes

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