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AI Lifecycle Management

Welcome to the Fourth Track, titled "AI Lifecycle Management". This track explores AI lifecycle management, detailing the iterative process from a business problem to an AI solution. It covers five key stages: prioritization and planning, understanding the data, building across agile sprints, model deployment, and managing and trusting the model. The track also emphasizes the importance of keeping humans involved in every stage of AI lifecycle management, with an example from Air Canada.

Issued on

September 14, 2024

Expires on

Does not expire