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Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

So I was able to get from growth hacking to data analytics, then data analytics to data science, and then data science to MLOps. In this episode, Mikiko Bazeley shares her learnings from building the ML Platform at Mailchimp. With us today on the episode is our guest, Mikiko Bazeley. Mikiko is a very well-known figure in the data community.

ML 52
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How to Version Control Data in ML for Various Data Sources

The MLOps Blog

For example, the first thing that we need to do is to initialize Git and DVC in our directory: git init dvc init Next, we add a data file to DVC tracking using the command: dvc add data/data.csv After that, DVC will instruct us to add new two files that have been generated after the last command: git add data/.gitignore DVC Git LFS neptune.ai

ML 52
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Deploying ML Models on GPU With Kyle Morris

The MLOps Blog

You’ll learn about: 1 Things to know before starting out with deploying GPUs 2 GPU tools and their utility 3 Common misconceptions around GPUs 4 Getting the most out of GPUs 5 Best GPU use-cases 6 Managing trade-offs with GPUs 7 …and more. I’d say the name of the game with GPUs is latency.

ML 52