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:
- Business and Data Understanding
- Data Preparation
- Model Engineering
- Model Evaluation
- Model Deployment
- 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.