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