AI Project Lifecycle

• Produce a clean, high-quality data set whose relationship to the target variables is understood. Locate the data set in the appropriate analytics environment so you are ready to model.
• Develop a solution architecture of the data pipeline that refreshes and scores the data regularly.

• Specify the key variables that are to serve as the model targets and whose related metrics are used determine the success of the project.
• Identify the relevant data sources that the business has access to or needs to obtain.

• Determine the optimal data features for the machine-learning model.
• Create an informative machine-learning model that predicts the target most accurately.
• Create a machine-learning model that’s suitable for production.

Deploy models with a data pipeline to a production or production-like environment for final user acceptance.

Finalize project deliverables: Confirm that the pipeline, the model, and their deployment in a production environment satisfy the customer’s objectives.