The Machine Learning Lifecycle

摘要： Learn about the standard process for building sustainable machine learning applications.

There are no standard practices for building and managing machine learning (ML) applications. As a result, machine learning projects are not well organized, lack reproducibility, and are prone to complete failure in the long run. We need a model that helps us maintain quality, sustainability, robustness, and cost management throughout the ML life cycle.

▲圖片來源：kdnuggets

The Cross-Industry Standard Process for the development of Machine Learning applications with Quality assurance methodology (CRISP-ML(Q)) is an upgraded version of CRISP-DM to ensure quality ML products.

轉貼自： kdnuggets.com

若喜歡本文，請關注我們的臉書 Please Like our Facebook Page： Big Data In Finance