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Google Cloud Platform for Machine Learning Essential Training

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Machine learning can make your applications faster and more intelligent. You can analyze customer data such as voice and text input, images, and video, and take action without human intervention. Google Cloud Platform (GCP) offers a competitive set of machine learning services for nearly every type of architecture, including serverless computing, containers, and virtual machines. In this course with instructor Lynn Langit, learn to use machine learning model development tools and services available in Google Cloud. Lynn shows how you can use Vertex AI machine learning services to develop, train, evaluate and host custom machine learning models. Learn how you can bring your own models or use the recently released generative AI foundational models as a basis for your work. Discover how new tools like Google AI Studio can get you up and running quickly, and see how to use the Vertex AI APIs to master end-to-end MLOps.


01 - Introduction
  • 01 - GCP and Machine Learning
  • 02 - What you should know
  • 03 - About using cloud services

  • 02 - 1. Vertex AI Studio
  • 01 - Use Vertex AI Model Garden
  • 02 - Design and test language model prompts
  • 03 - Design and test multimodal model prompts
  • 04 - Test image model generative output
  • 05 - Design and test speech generative output
  • 06 - Challenge Select and test GenAI models
  • 07 - Solution Select and test GenAI models

  • 03 - 2. Vertex AI Notebooks
  • 01 - Understand available services
  • 02 - Use TensorFlow example MNIST
  • 03 - Use managed and user-managed notebooks
  • 04 - Update notebook instance
  • 05 - Use notebook instances
  • 06 - Challenge Setup notebook
  • 07 - Solution Setup notebook

  • 04 - 3. Model Development
  • 01 - Understand Vector Search
  • 02 - Use Vector Search
  • 03 - Understand Feature Store
  • 04 - Challenge Create a Feature Store
  • 05 - Solution Create a Feature Store

  • 05 - 4. Model Deployment
  • 01 - Use the model registry
  • 02 - Register a model in the registry
  • 03 - Review batch and online endpoints
  • 04 - Understand model pipeline templates
  • 05 - Challenge Run and evaluate a model pipeline job
  • 06 - Solution Run and evaluate a model pipeline job

  • 06 - Conclusion
  • 01 - Next steps
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