Welcome to the TFX documentation section! TFX (TensorFlow Extended) is an end-to-end platform for deploying machine learning models. Below, you will find essential information and resources to get started with TFX.
Overview
TFX is designed to help data scientists and ML engineers build high-quality machine learning models more efficiently. It provides a set of components that can be used to create a reproducible and scalable ML pipeline.
- Components: TFX includes components like
Taxi,Dataflow,Dagster, and more. - Scalability: TFX is built to handle large-scale data processing and model training.
- Reproducibility: TFX ensures that your ML pipeline can be reproduced with the same results.
Getting Started
To get started with TFX, you can follow these steps:
Components
Here's a brief overview of some key TFX components:
- Taxi: A command-line interface to run TFX jobs.
- Dataflow: A component for large-scale data processing.
- Dagster: A general-purpose DAG execution platform.
For more details on each component, visit the TFX components page.
Resources
- TFX GitHub repository: Find the source code and documentation for TFX.
- TensorFlow Extended documentation: Learn more about TFX features and how to use them.
Image Gallery
Here are some images related to machine learning and data processing:
For more images and resources, check out our Machine Learning Resources.