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
Overview
Roles
Team Structure
Managing Projects
Hiring
Training and Debugging
Testing and Deployment
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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Overview
Why is running a Machine Learning team hard?
Overview - ML Teams

Summary

  • Machine Learning talents are expensive and scarce.
  • Machine Learning teams have a diverse set of roles.
  • Machine Learning projects have unclear timelines and high uncertainty.
  • Machine Learning is also the “high-interest credit card of technical debt."
  • Leadership often doesn’t understand Machine Learning.
Course Content - Previous
Machine Learning Teams
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Roles
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Contents
Summary