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As demands for AI applications grow, we’ve seen a lot of effort put by companies to build their Machine Learning Engineering (MLE) tools tailored for their needs. There are just so many challenges faced by industries in regards to having a well-designed environment for their Machine Learning (ML) lifecycle: building, deploying, and managing ML models in production. This post will cover two papers, explaining MLE practices from two of the leading tech companies: Google and Microsoft.
Going over some problem sets that cover basic Probability and Statistics (ProbStat) for Machine Learning Hi everyone, welcome to my second post! This is going to be the continuation from my first post. We’re going to be technical here, or should I say somewhat “Mathy”. Thus, for you who have been away for some time from hands-on Math, I suggest you review the material around Probability, Statistics, and Calculus.
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