Issue #2: Nuts and Bolts of ML. Unfriendly Comments. Great Expectations. Common ML Misconceptions.
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MLOps is a broad discipline that is moving quickly, and hopefully, that is reflected in the breadth of content we cover in this issue. From timeless lessons from Andrew Ng on applying Machine Learning, to an instructive case study on building an ML system at Stack Overflow, and from a new tool focused on data quality to a Twitter thread discussing misconceptions about ML in production, we hope you find something to your interest.
Issue #2: Nuts and Bolts of ML. Unfriendly Comments. Great Expectations. Common ML Misconceptions.
Issue #2: Nuts and Bolts of ML. Unfriendly…
Issue #2: Nuts and Bolts of ML. Unfriendly Comments. Great Expectations. Common ML Misconceptions.
MLOps is a broad discipline that is moving quickly, and hopefully, that is reflected in the breadth of content we cover in this issue. From timeless lessons from Andrew Ng on applying Machine Learning, to an instructive case study on building an ML system at Stack Overflow, and from a new tool focused on data quality to a Twitter thread discussing misconceptions about ML in production, we hope you find something to your interest.