Google Cloud Skills Boost

How Google Does Machine Learning

1 day Introductory universal_currency_alt 10 Credits

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.

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Course Info

Objectives
  • Describe Vertex AI Platform and how it's used to quickly build, train, and deploy AutoML machine learning models without writing a single line of code
  • Describe best practices for implementing machine learning on Google Cloud
  • Leverage Google Cloud Platform tools and environment to do ML
  • Articulate Responsible AI best practices
Prerequisites
• Some familiarity with basic machine learning concepts • Basic proficiency with a scripting language: Python preferred
Audience
• Aspiring machine learning data scientists and engineers • Machine learning scientists, data scientists, and data analysts • Data engineers
Available languages
English
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