Business understanding
Defining well the problem to be solved or opportunity to be developed provides not only the project scope, but also helps better understand the objectives of your AI initiative and what the successful completion of the project looks like.
Data understanding
Data collection from all the relevant sources, data exploration, assessment of its quality and integrity.
Data preparation
During this step the data is cleaned (inconsistencies and errors are addressed) and formatted. The data can also be transformed or preprocessed otherwise, as required.
Modeling
Finding the right algorithm/combination of algorithms for the task at hand, training and evaluating performance of the model.
Evaluation
Evaluating the results of the AI project, including the models, findings and conclusions generated. Here the team is able to see whether the produced results meet the business objectives and requirements, and identify areas for further work and improvement.
Deployment
When the solution is deemed to be ready it is deployed for production use. During this phase the results of the data mining project are implemented. That also includes any recommendations or decisions that have been made based on the results.
Monitoring & Improvements
It is crucial to see the model does not degrade over time - it needs consistent monitoring, and possibly retraining and changes. Monitoring the model is the way to make sure it stays relevant and accurate.