TensorFlow Extended - Machine Learning Pipelines and Model Understanding

TensorFlow Extended - Machine Learning Pipelines and Model Understanding

TensorFlow via YouTube Direct link

Introduction

1 of 40

1 of 40

Introduction

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Classroom Contents

TensorFlow Extended - Machine Learning Pipelines and Model Understanding

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  1. 1 Introduction
  2. 2 Overview
  3. 3 TFX Introduction
  4. 4 Accessibility
  5. 5 Contract
  6. 6 Systems View
  7. 7 Monitoring Systems
  8. 8 Building Better Software
  9. 9 User Journey
  10. 10 Data Quality
  11. 11 Massage
  12. 12 Validation
  13. 13 Separation
  14. 14 Components
  15. 15 How many know TensorFlow
  16. 16 Model Validator
  17. 17 MLMetadata
  18. 18 Component Configuration
  19. 19 Dependency Graph
  20. 20 DataDependency Graph
  21. 21 Metadata Store
  22. 22 Lineage
  23. 23 Warm Starting
  24. 24 Model Analysis
  25. 25 Reuse Components
  26. 26 Developing Models
  27. 27 Open Source Orchestrators
  28. 28 Putting it all together
  29. 29 Model Understanding
  30. 30 What Went Wrong
  31. 31 Data Validation
  32. 32 Data Validation Example
  33. 33 Questions
  34. 34 Slices
  35. 35 Whatif
  36. 36 CTR
  37. 37 Custom Executor
  38. 38 Assumptions
  39. 39 Baseline Trainer
  40. 40 Conclusion

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