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. Setting up Machine Learning Projects

Archetypes

What are the different archetypes of machine learning projects?

PreviousPrioritizingNextMetrics

Last updated 5 years ago

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  • Archetype 1 - Projects that improve an existing process: improving route optimization in a ride-sharing service, building a customized churn prevention model, building a better video game AI.

  • Archetype 2 - Projects that augment a manual process: turning mockup designs into application UI, building a sentence auto-completion feature, helping a doctor to do his/her job more efficient.

  • Archetype 3 - Projects that automate a manual process: developing autonomous vehicles, automating customer service, automating website design.

  • Data flywheels is a concept saying that more users lead to more data, more data lead to better models, and better models lead to more users.

Archetypes - ML Projects