How to Run an AI Project Retrospective
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Deploying an AI system is not the final step in an AI project. Before the team moves on, it should examine what worked, what caused problems, and what needs to change. A conventional retrospective often focuses on scope, schedules, budgets, and communication. Those topics remain important, but AI projects also introduce questions about data quality, model performance, infrastructure, monitoring, governance, and business value. Revisit the Project’s Purpose Start by reviewing the problem the…
1Key Takeaways
- Deploying an AI system is not the final step in an AI project.
- Before the team moves on, it should examine what worked, what caused problems, and what needs to change.
- A conventional retrospective often focuses on scope, schedules, budgets, and communication.
- Those topics remain important, but AI projects also introduce questions about data quality, model performance, infrastructure, monitoring, governance, and business value.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that deploying an AI system is not the final step in an AI project.
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