Machine Learning Life Cycle

The Machine Learning (ML) lifecycle is a process that guides the development and deployment of ML models. It is a series of steps that must be followed in order to ensure that the models are accurate, efficient, and reliable.

Business goal

An enterprise thinking about using ML should be clear on the issue at hand and the potential financial benefits of finding a solution. You must be able to evaluate the business value in relation to certain business goals and success factors.

ML problem framing

In this stage, the business issue is presented as a machine learning issue: what should be predicted based on what has been observed (known as a label or target variable). A key step in this phase is deciding what to predict and how performance and error metrics need to be improved.

Data processing

Processing data into a readable format is necessary for training an appropriate ML model. Collecting data, preparing data, and feature engineering—the process of producing, manipulating, extracting, and choosing variables from data—are all phases in the processing of data.

Model development

Model building, training, tuning, and evaluation are all parts of the model development process. In order to develop models, a CI/CD pipeline must be established that automates the build, train, and release processes to staging and production environments.

Deployment

A model can be deployed into production once it has been trained, tuned, evaluated, and validated. After that, you can compare your conclusions and forecasts to the model.

Monitoring

Through early detection and mitigation, a model monitoring system makes sure that your model is maintaining the desired level of performance.

The machine learning lifecycle as described is used to create the Well-Architected ML lifecycle, which is depicted below figure and applies the Well-Architected Framework pillars to each of the lifecycle phases.