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ML Pipelines on Google Cloud

ML Pipelines on Google Cloud

magic_button Machine Learning Pipeline Machine Learning Model Training TensorFlow
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2 hours 15 minutes Advanced universal_currency_alt No cost
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.

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Course Info
Objectives
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  • Orchestrate model training and deployment with TFX and Cloud AI Platform.
  • Operate deployed machine learning models effectively and efficiently.
  • Perform continuous training using various frameworks (Scikit Learn, XGBoost, PyTorch) and orchestrate pipelines using Cloud Composer and MLFlow.
  • Integrate ML workflows with upstream and downstream data management workflows to maintain end-to-end lineage and metadata management.
Available languages
English, español (Latinoamérica), 日本語, français, 한국어, and português (Brasil)
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