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Google Cloud

How Google does Machine Learning

Google Cloud and Google via Coursera

Overview

What are best practices for implementing machine learning on Google Cloud? What is Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML machine learning models without writing a single line of code? What is machine learning, and what kinds of problems can it solve?

Google thinks about machine learning slightly differently: it’s about providing a unified platform for managed datasets, a feature store, a way to build, train, and deploy machine learning models without writing a single line of code, providing the ability to label data, create Workbench notebooks using frameworks such as TensorFlow, SciKit Learn, Pytorch, R, and others. Our Vertex AI Platform also includes the ability to train custom models, build component pipelines, and perform both online and batch predictions. We also discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important to not skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them.

Syllabus

  • Introduction to Course and Series
    • This module introduces the course series and the Google experts who will be teaching it.
  • What It Means to be AI-First
    • In this module, you explore building a data strategy around machine learning.
  • How Google Does ML
    • This module shares the organizational know-how Google has acquired over the years.
  • Machine Learning Development with Vertex AI
    • All machine learning starts with some type of goal - whether it be a business use case, academic use case, or goal you are trying to solve. This module reviews the process of determining whether the model is ready for production the “proof of concept” or “experimentation” phase.
  • Machine Learning Development with Vertex Notebooks
    • This module explores both managed notebooks and user-managed notebooks for machine learning development in Vertex AI.
  • Best Practices for Implementing Machine Learning on Vertex AI
    • This module reviews best practices for a number of different machine learning processes in Vertex AI.
  • Responsible AI Development
    • This module discusses why machine learning systems aren’t fair by default and some of the things you have to keep in mind as you infuse ML into your products.
  • Summary
    • This module is a summary of the How Google Does Machine Learning course.

Taught by

Google Cloud Training

Reviews

4.7 rating, based on 3 reviews

Start your review of How Google does Machine Learning

  • As the course name tells us, this 1-week course introduces Machine Learning products from Google, including how Google frames an ML problem, how Google pre-trained models in the cloud, and so on.

    But if you want to learn ML algorithms more deeply, this course is not suitable for you. And having a deep understanding of ML algorithms is important in the real working environment.
  • Dimitrios Tosidis completed this course, spending 8 hours a week on it and found the course difficulty to be easy.

    Very nice introduction to machine learning and the notebooks in the Google Cloud. I found the videos very interesting and the tests very clever. This was an extraordinary training.
  • Aseem Bansal

    Aseem Bansal completed this course, spending 8 hours a week on it and found the course difficulty to be easy.

    I already knew bit about google cloud so the parts related to that were not new for me. But the parts where they were talking ML strategy was totally new for me. Some of it like replacing rules by ML totally blew my mind. First time hearing that stuff.

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