Google Cloud Platform for Machine Learning Essential Training

1h 35mBeginner2024-02-26

Authors

Lynn Langit

Cloud Architect

Course details

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.

Skills covered

Google Cloud PlatformMachine LearningGoogleSoftware Development ToolsCloud PlatformsEssential TrainingArtificial Intelligence (AI)Cloud ComputingSoftware Development

Concepts

Introduction

GCP and Machine Learning
What you should know
About using cloud services

Vertex AI Studio

Use Vertex AI Model Garden
Design and test language model prompts
Design and test multimodal model prompts
Test image model generative output
Design and test speech generative output
Challenge - Select and test GenAI models
Solution - Select and test GenAI models

Vertex AI Notebooks

Understand available services
Use TensorFlow example - MNIST
Use managed and user-managed notebooks
Update notebook instance
Use notebook instances
Challenge - Setup notebook
Solution - Setup notebook

Model Development

Understand Vector Search
Use Vector Search
Understand Feature Store
Challenge - Create a Feature Store
Solution - Create a Feature Store

Model Deployment

Use the model registry
Register a model in the registry
Review batch and online endpoints
Understand model pipeline templates
Challenge - Run and evaluate a model pipeline job
Solution - Run and evaluate a model pipeline job

Conclusion

Next steps