ML Pipelines on Google Cloud
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX).
What you'll learn
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.
Table of contents
- Containerized Training Applications 3m
- Containerizing PyTorch, Scikit, and XGBoost Applications 1m
- KubeFlow & AI Platform Pipelines 2m
- Continuous Training 2m
- Lab Intro : Continuous Training with multiple SDKs 0m
- Lab: Continuous Training with TensorFlow, PyTorch, XGBoost, and Scikit Learn Models with Kubeflow and AI Platform Pipelines 0m
- What is Cloud Composer? 6m
- Core Concepts of Apache Airflow 9m
- Continuous Training Pipelines using Cloud Composer (data) 6m
- Continuous Training Pipelines using Cloud Composer (model) 4m
- Apache Airflow, Containers, and TFX 1m
- Lab Intro : Continuous Training Pipelines with Cloud Composer 0m
- Lab: Continuous Training Pipelines with Cloud Composer 0m
