Quick to Production: Integrating Spark and TensorFlow for Efficient MLOps

Quick to Production: Integrating Spark and TensorFlow for Efficient MLOps

Databricks via YouTube Direct link

TensorFlow Hub

24 of 41

24 of 41

TensorFlow Hub

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Quick to Production: Integrating Spark and TensorFlow for Efficient MLOps

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Introduction
  2. 2 Welcome
  3. 3 What is this topic
  4. 4 What we will cover
  5. 5 Who is this talk for
  6. 6 Why do we need deep learning
  7. 7 Deep learning solutions
  8. 8 Who we are
  9. 9 Previously
  10. 10 Feature Engineering on TensorFlow
  11. 11 Benefits of Spark
  12. 12 Spark Libraries
  13. 13 pandas udif
  14. 14 Spark ML
  15. 15 Spark Pipeline
  16. 16 Questions
  17. 17 TensorFlow
  18. 18 TensorFlow Data
  19. 19 TensorFlow Distribution
  20. 20 Data Distribution
  21. 21 TensorFlow Extended
  22. 22 TensorFlow Transform
  23. 23 TensorFlow Recommenders
  24. 24 TensorFlow Hub
  25. 25 Batch vs RealTime
  26. 26 Experiment Tracking
  27. 27 Model Management
  28. 28 Serve Models
  29. 29 Serve Endpoint
  30. 30 Combine Solutions
  31. 31 TensorFlow Record
  32. 32 Spark Library
  33. 33 TensorFlow Distributor
  34. 34 TensorFlow Distributor Code
  35. 35 TensorFlow Distributor Nodes
  36. 36 Saving Models
  37. 37 Batch Inference
  38. 38 Recap
  39. 39 Pros
  40. 40 Challenges
  41. 41 Data and AI Summit

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.