The Machine Learning Lifecycle

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.

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.

The CRISP-ML(Q) has six individual phases:

1. Business and Data Understanding
2. Data Preparation
3. Model Engineering
4. Model Evaluation
5. Model Deployment
6. Monitoring and Maintenance.

These phases require constant iteration and exploration for building better solutions. Even though there is an order in a framework, the output of the later phase can determine whether we have to re-examine the previous phase or not.

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