How to build an experiment tracker + other resources
Here's this month's portion of MLOps articles, case studies, and interviews. These are mostly things we published in April, but also some resources we came across and thought were worth sharing. Hope you'll find something interesting here!?
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>?How to Build an Experiment Tracking Tool ?-? Stephen Oladele sat down with?neptune.ai 's engineers and talked about building an experiment tracking tool for machine learning projects. The result is this awesome article that explains how to develop requirements for your experiment tracking tool, what the components of an ideal experiment tracking tool are, how to architect the backend layer, and much more.?
>?Building and Deploying CV Models: Lessons Learned From Computer Vision Engineer ?- In this post, Alessandro Lamberti , an ML Engineer at NTT Data Italia,?shares his own experiences and the hard-won insights he's gained from designing, building, and deploying cutting-edge CV models across various platforms like cloud, on-premise, and edge devices.?
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Guides & tutorials
>?ML Model Packaging ?- Model packaging is crucial to the ML development lifecycle. Getting it right can mean the difference between a successful deployment and a project that may never see the light of day. So in this guide, Brain John Jnr Aboze explores the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks.?
>?Building LLM applications for production ?- An excellent article by Chip Huyen . She talks about?the key challenges of productionizing LLM applications, how to compose multiple tasks with control flows and incorporate tools for more complex and powerful applications, as well as some of the promising use cases she's seen companies building on top of LLMs.
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Tools
领英推荐
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ML platform podcast: talks with ML practitioners?
>?We ran MLOps live podcast for over a year.?29 incredible Q&A sessions? with people doing ML and MLOps at a reasonable scale.?
But as the year progressed, we saw more and more interest in internal ML platforms.?This is why in Season 2, we are talking to top professionals who build and operate ML platforms in the industry.
The first episode of Season 2 will be out in a few weeks. But you can get a taste with this special episode, where our new hosts, Piotr Niedzwiedz , and Aurimas Griciūnas , discuss ML platforms, data versioning, feature stores, and more.?
And don't forget to subscribe to our YouTube profile to get notified when the?ML Platform podcast ?is out.?
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Okay, that's it for today. If you want to talk about these recommendations, send me an email or?join the MLOps community here , and find the?#neptune -ai channel?there.
Feel free to forward this newsletter to your friends and communities, if you find it useful!
Cheers!