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Async review

Instruction and application
In Progress

Recap core topics​

  • Unit 3: Managing ML projects for successful execution.

Unit 3: Managing ML projects for successful execution​

In Unit 3, you explored:

  • ML-specific project management methodologies.
  • Managing stakeholders in ML projects.
  • Resource allocation for ML initiatives.
  • Assessing and mitigating risks.
  • Planning and implementing ML projects.

Pause and think​

Thinking about what you covered in the previous unit, let's see if you can remember some of the main considerations when managing and deploying ML solutions.

What are the benefits of CRISP-DM over traditional project management methods for ML projects, and why?
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What factors would you consider when estimating computational resources to train and deploy an ML model?
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What are the common risks to look out for in ML projects?
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In progress