Módulo 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.
Módulo 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.
Módulo 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.
Módulo 4: ML Metadata with TFX
Access and analyze pipeline artifacts in ML Metadata store.
Módulo 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
Módulo 6: Continuous Training with Cloud Composer
Perform continuous training with Cloud Composer
Módulo 7: ML Pipelines with MLflow
Manage Machine Learning lifecycle with MLflow
Módulo 8: Summary
Summarize the course