Full Stack Deep Learning
  • Full Stack Deep Learning
  • Course Content
    • Setting up Machine Learning Projects
      • Overview
      • Lifecycle
      • Prioritizing
      • Archetypes
      • Metrics
      • Baselines
    • Infrastructure and Tooling
      • Overview
      • Software Engineering
      • Computing and GPUs
      • Resource Management
      • Frameworks and Distributed Training
      • Experiment Management
      • Hyperparameter Tuning
      • All-in-one Solutions
    • Data Management
      • Overview
      • Sources
      • Labeling
      • Storage
      • Versioning
      • Processing
    • Machine Learning Teams
      • Overview
      • Roles
      • Team Structure
      • Managing Projects
      • Hiring
    • Training and Debugging
      • Overview
      • Start Simple
      • Debug
      • Evaluate
      • Improve
      • Tune
      • Conclusion
    • Testing and Deployment
      • Project Structure
      • ML Test Score
      • CI / Testing
      • Docker
      • Web Deployment
      • Monitoring
      • Hardware/Mobile
    • Research Areas
    • Labs
    • Where to go next
  • Guest Lectures
    • Xavier Amatriain (Curai)
    • Chip Huyen (Snorkel)
    • Lukas Biewald (Weights & Biases)
    • Jeremy Howard (Fast.ai)
    • Richard Socher (Salesforce)
    • Raquel Urtasun (Uber ATG)
    • Yangqing Jia (Alibaba)
    • Andrej Karpathy (Tesla)
    • Jai Ranganathan (KeepTruckin)
    • Franziska Bell (Toyota Research)
  • Corporate Training and Certification
    • Corporate Training
    • Certification
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  1. Course Content
  2. Machine Learning Teams

Managing Projects

How to manage machine learning projects properly?

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Last updated 4 years ago

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Summary

  • Manage Machine Learning projects can be very challenging:

    • In Machine Learning, it is hard to tell in advance what’s hard and what’s easy.

    • Machine Learning progress is nonlinear.

    • There are cultural gaps between research and engineering because of different values, backgrounds, goals, and norms.

    • Often, leadership just does not understand it.

  • The secret sauce is to plan the Machine Learning project probabilistically!

    • Attempt a portfolio of approaches.

    • Measure progress based on inputs, not results.

    • Have researchers and engineers work together.

    • Get end-to-end pipelines together quickly to demonstrate quick wins.

    • Educate leadership on Machine Learning timeline uncertainty.

Managing - ML Teams