Full Stack Deep Learning
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Full Stack Deep Learning
Course Content
Setting up Machine Learning Projects
Overview
Lifecycle
Prioritizing
Archetypes
Metrics
Baselines
Infrastructure and Tooling
Data Management
Machine Learning Teams
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
According to a 2019 report, 85% of AI projects fail to deliver on their intended promises to business. Why do so many projects fail?
Overview - ML Projects

Summary

  • ML is still research, therefore it is very challenging to aim for 100% success rate.
  • Many ML projects are technically infeasible or poorly scoped.
  • Many ML projects never make the leap into production.
  • Many ML projects have unclear success criteria.
  • Many ML projects are poorly managed.
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Setting up Machine Learning Projects
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Lifecycle
Last modified 2yr ago
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