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Google Cloud

Machine Learning in the Enterprise

Google Cloud and Google via Coursera


This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks.

The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to exporting a trained model.

You will build a custom training machine learning model, which allows you to build a container image with little knowledge of Docker.

The case study team examines hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Vertex AI can be used to manage ML models.


  • Introduction
    • This module provides an overview of the course and its objectives.
  • Understanding the ML Enterprise Workflow
    • This module discusses the ML enterprise workflow and the purpose of each step.
  • Data in the Enterprise
    • This module reviews Google’s enterprise data management and governance tools: Feature Store, Data Catalog, Dataplex, and Analytics Hub.
  • Science of Machine Learning and Custom Training
    • This module reviews the art and science of machine learning and neural networks. We'll also discuss how to train custom ML models using Vertex AI.
  • Vertex Vizier Hyperparameter Tuning
    • In this module we discuss how to do hyperparameter tuning using Vertex AI Vizier.
  • Prediction and Model Monitoring Using Vertex AI
    • This module covers Vertex AI prediction and model monitoring. We'll first discuss batch and online predictions using pre-built and custom containers, then we'll review model monitoring, which is a service that helps manage the performance of your ML models.
  • Vertex AI Pipelines
    • This module discusses Vertex AI pipelines and how to build them to orchestrate your ML workflow.
  • Best Practices for ML Development
    • This module reviews best practices for a number of different machine learning processes in Vertex AI.
  • Course Summary
    • This module is a summary of the Machine Learning in the Enterprise course.
  • Series Summary
    • This module is a summary of the Machine Learning on Google Cloud course series.

Taught by

Google Cloud Training


4.6 rating at Coursera based on 1401 ratings

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