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Set the foundations for a responsible, valuable and feasible AI solution.
Establishes strategic, operational, and ethical foundations for any AI initiative. Define the problem, intended use cases and users, and ensure the solution is justified, feasible, aligned with organizational priorities and stakeholder governance-ready.
Generate, collect, purchase or scrape the data required to build the AI solution. This includes discovering available datasets, identifying data quality problems, and deriving initial insights into the data and perspectives on a data plan.
Build, test and validate the AI system using reliable data and appropriate methods.
Address quality issues and reconstruct the dataset from the initial raw data into a format the model can use, ensuring required criteria are met. This helps ensure that the model achieves the goals established in the problem definition phase.
An iterative process where a variety of models are derived, tested, and refined until an appropriate model that meets the desired criteria is created.
Evaluate whether the developed AI model efficiently performs against defined metrics and requirements.
Release, operate and continually improve the AI system in real-world use, and plan for its end-of-life.
Once a model has been developed to meet the expected outcome and performs at a level determined to be ready for use in a real-world environment, deploy it into a production environment.
Continuously monitor performance, safety, and real-world impacts, and maintain the system. Use feedback and new data to identify issues, make improvements, and ensure the system remains effective, safe and compliant over time.
All models eventually reach their end-of-life. Planning for retirement is a sign of a mature AI strategy.
Learn from real-world use and stakeholder feedback to inform future iterations across the lifecycle.
