AI, generative AI, and ML overview
Leverage the power of AI/ML models and solutions to transform your organization and solve real-world problems.
Explore AI, generative AI, and ML in Google Cloud
Read documentation and Cloud Architecture Center articles about AI, generative AI, and ML products, capabilities, and procedures.
Overview of generative AI on Vertex AI
Access Google's large generative AI models so you can test, tune, and deploy them for use in your AI-powered applications.
Explore AI models in Model Garden
Discover test, customize, and deploy Google proprietary and select OSS models and assets from an ML model library.
Build a generative AI application on Google Cloud
Learn the stages of building a generative AI application, choose the best products and tools for your use case, and access the documentation you need to get started.
Introduction to machine learning on Vertex AI
Support data engineering, data science, and ML engineering workflows on a unified platform, enabling you to train ML models and deploy AI solutions.
AI and ML architecture resources
Plan your approach with architecture center resources across a wide variety of AI & ML subjects. (Goes to Architecture Center.)
Best practices for implementing ML
Plan for implementing ML, with a focus on custom-trained models based on your data and code. (Goes to Architecture Center.)
Training, blog articles, and more
Go to training courses, blog articles, and other related resources.
Develop gen AI apps on local CPUs
Go to gain a conceptual understanding and then practice applying RLHF to tune an LLM. (External site)
Applied AI summit learning path
Study Vertex AI and Gemini in Google Cloud. (Goes to Google Cloud Skills Boost.)
Introduction to generative AI learning path
Study generative AI concepts, from the fundamentals of large language models to responsible AI principles. (Goes to Google Cloud Skills Boost.)
Generative AI for developers learning path
Study generative AI with a technical focus, designed for App Developers, ML Engineers, and Data Scientists. (Goes to Google Cloud Skills Boost.)
Machine learning engineer learning path
Study designing, building, productionalizing, optimizing, operating, and maintaining ML systems. (Goes to Google Cloud Skills Boost.)
Reinforcement learning from human feedback
Go to gain a conceptual understanding and then practice applying RLHF to tune an LLM. (Goes to external website.)