This course provides a foundational understanding of the full AI development lifecycle — from defining the problem and collecting data, through to model training, deployment, and ongoing monitoring. It also addresses the governance, transparency, security, and ethical considerations that are central to responsible AI use.
Learning Outcomes:
- Define an AI problem, classify it appropriately, identify required expertise, and ensure AI is used responsibly and ethically.
- Choose data collection methods, assess data quality and representativeness, prepare and engineer features, and document data decisions.
- Evaluate and select AI algorithms, train and tune models, assess performance, identify bias, and obtain stakeholder approval.
- Design and implement a production pipeline, integrate AI with applications, and support the deployed AI solution.
- Monitor AI systems in production, assess business impact, measure community effects, handle user feedback, and decide when to retrain or decommission.
