Course Outline

Introduction

Setting up TensorFlow Extended (TFX)

Overview of TFX Features and Architecture

Understanding Pipelines and Components

Working with TFX Components

Ingesting Data

Validating Data

Tranforming a Data Set

Analyzing a Model

Feature Engineering

Training a Model

Orchestrating a TFX Pipeline

Managing Meta Data for ML Pipelines

Model Versioning with TensorFlow Serving

Deploying a Model to Production

Troubleshooting

Summary and Conclusion

Requirements

  • An understanding of DevOps concepts
  • Machine learning development experience
  • Python programming experience

Audience

  • Data scientists
  • ML engineers
  • Operation engineers
 21 Hours

Testimonials (1)