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 LearningSoftware Development ToolsGoogleCloud PlatformsEssential TrainingArtificial Intelligence (AI)Cloud ComputingSoftware Development
Concepts
0. Introduction
- 01 - GCP and Machine Learning
- 02 - What you should know
- 03 - About using cloud services
1. Vertex AI Studio
- 04 - Use Vertex AI Model Garden
- 05 - Design and test language model prompts
- 06 - Design and test multimodal model prompts
- 07 - Test image model generative output
- 08 - Design and test speech generative output
- 09 - Challenge - Select and test GenAI models
- 10 - Solution - Select and test GenAI models
2. Vertex AI Notebooks
- 11 - Understand available services
- 12 - Use TensorFlow example - MNIST
- 13 - Use managed and user-managed notebooks
- 14 - Update notebook instance
- 15 - Use notebook instances
- 16 - Challenge - Setup notebook
- 17 - Solution - Setup notebook
3. Model Development
- 18 - Understand Vector Search
- 19 - Use Vector Search
- 20 - Understand Feature Store
- 21 - Challenge - Create a Feature Store
- 22 - Solution - Create a Feature Store
4. Model Deployment
- 23 - Use the model registry
- 24 - Register a model in the registry
- 25 - Review batch and online endpoints
- 26 - Understand model pipeline templates
- 27 - Challenge - Run and evaluate a model pipeline job
- 28 - Solution - Run and evaluate a model pipeline job
Conclusion
- 29 - Next steps
Related courses
- Google Cloud Platform for Machine Learning Essential Training (2018)
- Google Cloud Platform Essential Training for Administrators (2020)
- Google Cloud Platform Essential Training for Administrators
- Complete Guide to Google BigQuery for Data and ML Engineers
- Essential Google Cloud Training: Deploy, Analyze, and Secure Your Cloud Environment
- Cloud Computing Careers and Certifications
- Google Cloud Professional Machine Learning Engineer Cert Prep
- Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications
Related learn paths
- Develop Your AI Skills with Google Gemini and Google Cloud Platform
- Advance Your Skills in AI and Machine Learning
- Build Your Knowledge of Cloud Administration
- Prepare for the AWS Certified Developer Associate (DVA-C01) Certification Exam
- Foundational AI Skills for Azure Administration
- Understanding Generative AI for Tech Leaders
- Explore a Career in Data Engineering
- MLOps Essentials for Developers and AI Engineers: Tools, Pipelines, Security