Course Code
tfx
Duration
21 hours (usually 3 days including breaks)
Requirements
- An understanding of DevOps concepts
- Machine learning development experience
- Python programming experience
Audience
- Data scientists
- ML engineers
- Operation engineers
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.
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
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