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It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.
Most of the code is from my General Assembly capstone project - where I go through the process of consuming data I have pulled from a public API, do a bit of feature engineering, integration with another popular Machine Learning tool called Comet ML and Github Actions, then train multiple algorithms in parallel, all repeatable since Metaflow keeps track of all experiment metadata.
This article comes with a simple example project and although the algorithms it needs don't require the resources that a more complicated model requires, it represents real world data, and was originally created when I started following Formula 1 more regularly and was looking for something that I can learn Data Science and ML with, and wouldn't mind spending countless of hours with.
However, if you want a more realistic problem, more worked-out open source examples are found here, and here, and hopefully you will believe me that you don't need to be the size of Google to be able to tackle these types of Data Science problems.
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