Introduction to Generative AI for Decision Makers
3 HOURS
This course is designed for business users, business leaders, and, more generally, decision makers within organizations who are seeking to gain a comprehensive understanding of generative AI and its potential impact on their businesses. The overall goal of the course is to educate decision makers on the potential impact generative AI can have in their organizations, educate them about generative AI services on Google Cloud, and to inspire decision makers to start thinking about what is possible with generative AI technologies.
Empower Decision Makers with Generative AI
1 DAY
This course is designed for business users, business leaders, and decision makers who want to understand the transformative potential of generative AI and its impact on their organizations. You’ll gain a comprehensive understanding of this technology, learn how it can be leveraged to drive innovation and efficiency, and explore the range of generative AI services available on Google Cloud. By the end of this course, you’ll be equipped to make informed decisions about implementing AI solutions.
Industry Use Cases for Generative AI
2 HOURS
This course is designed for business professionals and technical individuals who are interested in understanding the practical applications of generative AI in various industries. This course targets professionals across different roles, including business analysts, data scientists, software developers, business users, and decision-makers who are specifically interested in leveraging generative AI for business solutions.
Generative AI Explorer
3 HOURS
Explore Vertex AI Studio and the PaLM API in Vertex AI, all while taking part in a friendly competition! You will create and test a prompt, create a conversation, and explore the prompt gallery through hands-on activities. After completing these tasks, you will compete to be the first to finish a challenge lab using what you have learned from earlier labs.
Introduction to Responsible AI in Practice
1 DAY
In this course, you will do a high-level exploration of Google’s recommended best practices for responsible AI usage across different areas of focus: Fairness, Interpretability, Privacy, and Safety. Along the way, you will learn how you can leverage different open-source tools and tools on Vertex AI to explore these concepts and spend time considering the different challenges that arise with generative AI.
Vertex AI Agent Builder
1 DAY
In this course, you learn how to use Vertex AI Agent Builder to create search engines and chat applications. You will then explore how to integrate these search engineers and chat applications into your own applications. Finally, you learn how to manage the tools built in Vertex AI Agent Builder in production
Introduction to Developer Efficiency on Google Cloud
1 DAY
In this course, you are introduced to how generative AI can be used to make developers more efficient at writing code and implementing new features into applications. Along the way, you learn principles of prompt design and how to explore the different models available on Google Cloud by using Vertex AI Model Garden.
Application Development with LLMs on Google Cloud
1 DAY
In this course, you explore tools and APIs available on Google Cloud for integrating large language models (LLMs) into your application. After exploring generative AI options on Google Cloud, next you explore LLMs and prompt design in Vertex AI Studio. Then you learn about LangChain, an open-source framework for developing applications powered by language models. After a discussion around more advanced prompt engineering techniques, you put it all together to build a multi-turn chat application by using LangChain and the Vertex AI PaLM API.
Generative AI in Production
1 DAY
In this course, you will learn about the different challenges that arise when productionizing generative AI-powered applications versus traditional ML. You will learn how to manage experimentation and tuning of your LLMs, then you will discuss how to deploy, test and maintain your LLM-powered applications. Finally, you will discuss best practices for logging and monitoring your LLM-powered applications in production.
