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Mastering the Lifecycle of AI Projects for Optimal Outcomes
Today we will be discussing the lifecycle of AI projects and how they are undertaken, both internally and externally. The focus will be on why certain internal AI projects fail to meet the expected outcomes, and how organizations can avoid falling into these traps.
One of the biggest factors affecting the success of internal AI projects is the quality of data. Gathering complete data, including metadata, is crucial to
developing accurate AI models.
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By Apogee Suite
5
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Today we will be discussing the lifecycle of AI projects and how they are undertaken, both internally and externally. The focus will be on why certain internal AI projects fail to meet the expected outcomes, and how organizations can avoid falling into these traps.
One of the biggest factors affecting the success of internal AI projects is the quality of data. Gathering complete data, including metadata, is crucial to
developing accurate AI models.