Content

The course includes presentations, demonstrations, and hands-on labs.

Module 1: Introduction to TFX

• Develop a high level understanding of TFX standard pipeline components.
• Learn how to use a TFX Interactive Context for prototype development of TFX pipelines.
• Work with the Tensorflow Data Validation (TFDV) library to check and analyze input data.
• Utilize the Tensorflow Transform (TFT) library for scalable data preprocessing and feature transformations.
• Use the KerasTuner library for model hyperparameter tuning.
• Employ the Tensorflow Model Analysis (TFMA) library for model evaluation.

Module 2: Pipeline orchestration with TFX

• Use the TFX CLI and Kubeflow UI to build and deploy TFX pipelines to a hosted AI Platform Pipelines instance on Google Cloud.
• Deploy a TensorFlow model trained using AI Platform Training to AI Platform Prediction.
• Perform advanced distributed hyperparameter tuning using CloudTuner and Cloud AI Platform Vizier.

Module 3: Custom components and CI/CD for TFX pipelines

• Develop a CI/CD workflow with Cloud Build to build and deploy a TFX Pipeline.
• Integrate Github trigger to trigger Cloud Build CI/CD workflow for a TFX pipeline.

Module 4: ML Metadata with TFX

• Access and analyze pipeline artifacts in ML Metadata store.

Module 5: Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines

• Perform continuous training with Scikit-learn and AI Platform Pipelines.
• Perform continuous training with PyTorch and AI Platform Pipelines.
• Perform continuous training with XGBoost and AI Platform Pipelines.
• Perform continuous training with TensorFlow and AI Platform Pipelines.

Module 6: Continuous Training with Cloud Composer

• Perform continuous training with Cloud Composer.

Module 7: ML Pipelines with MLflow

• Manage Machine Learning lifecycle with MLflow.

Module 8: Summary

• Summarize the course.