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. Testing and Deployment

Project Structure

What are the different components of a machine learning system?

PreviousTesting and DeploymentNextML Test Score

Last updated 4 years ago

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Summary

  • The prediction system involves code to process input data, to construct networks with trained weights, and to make predictions.

  • The training system processes raw data, runs experiments, and manages results.

  • The goal of any prediction system is to be deployed into the serving system. Its purpose is to serve predictions and to scale to demand.

  • Training and validation data are used in conjunction with the training system to generate the prediction system.

  • At production time, we have production data that has not been seen before and can only be served by the serving system.

  • The prediction system should be tested by functionality to catch code regressions and by validation to catch model regressions.

  • The training system should have its tests to catch upstream regressions (change in data sources, upgrade of dependencies)

  • For production data, we need monitoring that raises alert to downtime, errors, distribution shifts, etc. and catches service and data regressions.

Project Structure - Testing and Deployment