Full Stack Deep Learning
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Full Stack Deep Learning
Course Content
Setting up Machine Learning Projects
Infrastructure and Tooling
Data Management
Machine Learning Teams
Training and Debugging
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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ML Test Score
How can you test your machine learning system?
ML Test Score - Testing and Deployment

Summary

  • ​ML Test Score :  A Rubric for Production Readiness and Technical Debt Reduction  is an exhaustive framework/checklist from practitioners at Google.
  • The paper presents a rubric as a set of 28 actionable tests and offers a scoring system to measure how ready for production a given machine learning system is. These are categorized into 4 sections: (1) data tests, (2) model tests, (3) ML infrastructure tests, and (4) monitoring tests.
  • The scoring system provides a vector for incentivizing ML system developers to achieve stable levels of reliability by providing a clear indicator of readiness and clear guidelines for how to improve.
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