Overview
TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.
By the end of this training, participants will be able to:
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Requirements
- An understanding of DevOps concepts
- Machine learning development experience
- Python programming experience
Audience
- Data scientists
- ML engineers
- Operation engineers
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