ML/AI platform build vs. buy decision + other resources
June's insights are here. This edition focuses on problems and solutions for scaling ML?systems, lessons learned when building ML/AI platforms (including the build vs. buy decision), vector database landscape, and more. ?
Enjoy!
Case studies & practical MLOps
>?MLOps Journey: Building a Mature ML Development Process - Let’s start with Albin Sundqvist 's story about building scalable, reliable ML systems through MLOps. He covers the most common problems and potential solutions with best practices based on his experience.
> ML/AI Platform Build vs. Buy Decision: What Factors to Consider - Moving on, we have an article from Luis Silva . Over the past decade, Luís has helped teams build ML/AI platforms from scratch and architected platforms integrating different cloud vendors and SaaS solutions. In this post, he summarizes his learnings to help understand what to think about when deciding whether to buy or build a platform.
Guides & tutorials
> How to Migrate From MLflow to neptune.ai?- If you have ever wondered how much work would be required to migrate from MLflow to Neptune, this hands-on guide by Axel Mendoza walks you through all the necessary steps. From how to transfer the data, and adapt training scripts to familiarizing team members with the new UI.
> How to Automate ML Experiment Management With CI/CD - Last but not least, in this blog post Kilian Kluge and Dhruvil Karani guide you through the process of automating ML experiments with GitHub Actions (GitHub's integrated CI/CD platform) and Neptune. Though centered on GitHub Actions, the concepts are relevant to other CI/CD frameworks too.
ML Platform Podcast
We have two new podcast episodes for you this month, now on YouTube. These episodes feature Hien Luu and Frank Liu .
You can also catch up on the latest bonus content from all podcast episodes here. And if you haven’t done so yet, subscribe to our YouTube channel to get notifications about new episodes. Season 3 is coming soon!
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Cheers!