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
Powered by GitBook
On this page

Was this helpful?

  1. Course Content
  2. Testing and Deployment

Monitoring

How to monitor your machine learning system?

PreviousWeb DeploymentNextHardware/Mobile

Last updated 5 years ago

Was this helpful?

Summary

  • It is crucial to monitor serving systems, training pipelines, and input data. A typical monitoring system can raise alarms when things go wrong and provide the records for tuning things.

  • Cloud providers have decent monitoring solutions.

  • Anything that can be logged can be monitored: dependency changes, distribution shift in data, model instabilities, etc.

  • Data distribution monitoring is an underserved need!

  • It is important to monitor the business uses of the model, not just its statistics. Furthermore, it is important to be able to contribute failures back to the dataset.

Monitoring - Testing and Deployment