You can watch our archived 4-week program led by gen AI experts and learn how to transform your business
Startup School: Gen AI will return with live trainings in October. Make sure to save your seat now.
Exclusive live, hands-on classes showcasing Google Cloud’s latest gen AI tools and solutions, including Gemini, to help you accelerate your business’ growth.
Google Cloud has been at the forefront of AI innovation for over two decades, seamlessly integrating AI into its core operations.
For Chapter 2 of Startup School, we’re going to take your gen AI knowledge to the next level and take you on a learning journey as our experts show you how to build AI agents that can streamline and optimize tasks, saving your business valuable time and money.
Together we’ll learn to leverage our generative AI products and solutions on Vertex AI, including Gemini, across a number of focus areas, including:
- Multimodality: Using text, images & videos with Gemini 1.5
- Connectivity: Integrate databases & APIs with Function Calling
- Agent Design: Build custom agents with Vertex AI and Firebase
- Workflows: Orchestrate agents with LangChain
- Productionization: Orchestration and scaling of agents
Note: Participants should have basic familiarity with (Python or JavaScript), Vertex AI, and Firebase from Week 2 onwards.
PROGRAM CURRICULUM
AMER / EMEA 10am PDT / 1pm EDT / 6pm GMT+1
May 21
-
Jun 13
search
Week 1
Week 2
Week 3
Week 4
Time
Title
Generative AI: From Prototype to Monetization
Hussain Chinoy, Meltem Subasioglu
It’s been a little over a year since Generative AI took the technology sector (and the whole world) by storm. Just in that time, we’ve already seen enormous improvements in the capabilities of large models as well as a burgeoning ecosystem of products built around them.
This session will provide a refreshed overview from our Q1 class on generative AI, covering the following topics:
- The Google model ecosystem: led by Gemini, our largest and most capable AI model incl.:
- What’s new in Gemini 1.5
- How to use larger context windows (up to 1 million tokens) effectively
- Multimodal use cases for businesses
- Function Calling in Gemini to connect to external systems
- More efficient training and serving, while increasing model performance
- Better understanding and reasoning across modalities
- Overview of AI on Google Cloud, including 1P and 3P foundation models: We will provide an overview of the AI services offered by Google Cloud, including foundation models from Google and third-party providers incl.
- Big partners and OSS projects in this space that our users are finding value with: LangChain, LlamaIndex, Chroma DB, Milvus, etc., and how to use them w/ Gemini
- Open and Partner models, including Llama, Claude 3, and more
- Limitations of generative AI: We will discuss the main limitations of generative AI, such as bias, frozen training data, and safety, and how we’re overcoming some of these limitations with larger context windows and function calling.
- Choosing the right model and approach: We’ll discuss best practices for using generative models, in terms of model selection and how to determine when to use off-the-shelf models vs. fine-tuned models.
- Protection with generative AI services: We’ll discuss our industry-first approach to indemnity for potential legal risks related to copyright infringement claims for both the training data used by Google and the outputs generated by customers
.
Products covered:
Cloud AI Generative AI Portfolio, Vertex AI, Model Garden, Gemini, LangChain, LlamaIndex
Who should attend:
This session is for Decision Makers (startup CxOs), Product Leaders and Practitioners (startup developers, data engineers).
90 Minutes
Hussain Chinoy
Technical Solutions Manager, Gen AI,
Google Cloud
Meltem Subasioglu
Senior Generative AI Solutions Architect,
Google Cloud
Deep dive into the Gen AI ecosystem
Gary Ng
In this session, we’ll take a closer look at all of the core components and design patterns in the Gen AI ecosystem that you’ll use in later sessions to build a generative AI app, focusing on a customer service chat app.
- What you’ll build: Chatbot for customer service that is grounded in your content and connects to your CRM, inventory, and support systems.
- Gen AI stack for developers: Model, tools & functions, orchestration, and deployment
- Backend and frontend: Backend development in Python and other SDKs vs. application development with Genkit
- Gen AI tools and patterns: Prompt tuning in Gemini, Retrieval Augmented Generation (RAG)
Products covered:
Gemini, Vertex AI Studio, Vertex AI Agent Builder, Document AI, Firebase, Firebase Extensions, Genkit
Who should attend:
This session is for Product Leaders and Practitioners (startup developers, data engineers)
90 Minutes
Gary Ng
Gen AI Field Solutions Architect,
Google Cloud
EMEA / APAC 9am GMT+1 / 1:30pm IST / 4pm SGT / 5pm KST/JST
May 21
-
Jun 13
search
Week 1
Week 2
Week 3
Week 4
Time
Title
Generative AI: From Prototype to Monetization
Hussain Chinoy, Meltem Subasioglu
It’s been a little over a year since Generative AI took the technology sector (and the whole world) by storm. Just in that time, we’ve already seen enormous improvements in the capabilities of large models as well as a burgeoning ecosystem of products built around them.
This session will provide a refreshed overview from our Q1 class on generative AI, covering the following topics:
- The Google model ecosystem: led by Gemini, our largest and most capable AI model incl.:
- What’s new in Gemini 1.5
- How to use larger context windows (up to 1 million tokens) effectively
- Multimodal use cases for businesses
- Function Calling in Gemini to connect to external systems
- More efficient training and serving, while increasing model performance
- Better understanding and reasoning across modalities
- Overview of AI on Google Cloud, including 1P and 3P foundation models: We will provide an overview of the AI services offered by Google Cloud, including foundation models from Google and third-party providers incl.
- Big partners and OSS projects in this space that our users are finding value with: LangChain, LlamaIndex, Chroma DB, Milvus, etc., and how to use them w/ Gemini
- Open and Partner models, including Llama, Claude 3, and more
- Limitations of generative AI: We will discuss the main limitations of generative AI, such as bias, frozen training data, and safety, and how we’re overcoming some of these limitations with larger context windows and function calling.
- Choosing the right model and approach: We’ll discuss best practices for using generative models, in terms of model selection and how to determine when to use off-the-shelf models vs. fine-tuned models.
- Protection with generative AI services: We’ll discuss our industry-first approach to indemnity for potential legal risks related to copyright infringement claims for both the training data used by Google and the outputs generated by customers.
Products covered:
Cloud AI Generative AI Portfolio, Vertex AI, Model Garden, Gemini, LangChain, LlamaIndex
Who should attend:
This session is for Decision Makers (startup CxOs), Product Leaders and Practitioners (startup developers, data engineers).
90 Minutes
Hussain Chinoy
Technical Solutions Manager, Gen AI,
Google Cloud
Meltem Subasioglu
Senior Generative AI Solutions Architect,
Google Cloud
Deep dive into the Gen AI ecosystem
Gopala Dhar
In this session, we’ll take a closer look at all of the core components and design patterns in the Gen AI ecosystem that you’ll use in later sessions to build a generative AI app, focusing on a customer service chat app.
- What you’ll build: Chatbot for customer service that is grounded in your content and connects to your CRM, inventory, and support systems.
- Gen AI stack for developers: Model, tools & functions, orchestration, and deployment
- Backend and frontend: Backend development in Python and other SDKs vs. application development with Genkit
- Gen AI tools and patterns: Prompt tuning in Gemini, Retrieval Augmented Generation (RAG)
Products covered:
Gemini, Vertex AI Studio, Vertex AI Agent Builder, Document AI, Firebase, Firebase Extensions, Genkit
Who should attend:
This session is for Product Leaders and Practitioners (startup developers, data engineers).
90 Minutes
Gopala Dhar
AI Engineer,
Global Business
