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Continuous Delivery for Machine Learning
Automating the end-to-end lifecycle of Machine Learning applications Machine Learning applications are becoming popular in our industry, however the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to change in three axis: the code itself, the model, and the data
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GitHub - The-Japan-DataScientist-Society/100knocks-preprocess: データサイエンス100本ノック（構造化データ加工編）
