TLDR
In this first post in our 2-part ML Ops series, we are going to look at ML Ops and highlight how and why data quality is key to ML Ops workflows. We are starting to treat ML like other software engineering disciplines that require processes and tooling to ensure seamless workflows and reliable outputs. The goal of ML Ops is to accelerate the development and production deployment of machine learning models while ensuring the quality of model outputs. Data quality, in particular, has been a consistent focus, as it often leads to issues that can go unnoticed for a long time, bring entire pipelines to a halt.via the TL;DR App
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Written by kyleweaton | Community Manager of Great Expectations
