TFX (TensorFlow Extended) is an end-to-end platform for deploying ML pipelines in production. Whether you're a beginner or an experienced developer, this guide will walk you through the essentials of building and deploying machine learning workflows with TFX.

🛠️ Core Components of TFX

TFX is built around a set of modular components that work together to streamline the ML pipeline process:

  1. TFX SDK - A library for defining and executing pipelines.
  2. TFX Components - Tools for data ingestion, preprocessing, training, evaluation, and serving.
  3. TFX Orchestration - Manages the execution of pipeline components.

📌 Tip: Start with the TFX Overview Tutorial to understand the platform's architecture before diving into hands-on projects.

📊 Example Workflow: From Data to Deployment

  1. Data Ingestion - Use ExampleGen to load datasets.
  2. Data Preprocessing - Apply transformations via Transform.
  3. Model Training - Train using Trainer and TensorFlow models.
  4. Model Evaluation - Validate performance with Evaluator.
  5. Model Serving - Deploy with Serving for real-time predictions.
TFX Workflow

🚀 Getting Started: Key Steps

  • Install TFX: 
    pip install tfx
  • Create a pipeline configuration file (pipeline.proto).
  • Run the pipeline using the TFX CLI: 
    tfx pipeline run --pipeline_config_path=PIPELINE_CONFIG_PATH
  • Monitor progress with the TFX Dashboard.

📘 Further Reading

For deeper insights into TFX components and best practices:

TFX Dashboard

Explore how TFX simplifies ML operations and scales with your project needs! 🌟