APIs dominate the web, accounting for the majority of all internet traffic. And more AI means more APIs, because they act as an important mechanism to move data into and out of AI applications, AI agents, and large language models (LLMs). So how can you make sure all of these APIs are secure? In this session, we’ll take you through OWASP’s top 10 API and LLM security risks, and show you how to mitigate these risks using Google Cloud’s security portfolio, including Apigee, Model Armor, Cloud Armor, Google Security Operations, and Security Command Center.

Bring your laptop and join us for an interactive demo on how to apply large language models (LLMs) from the Vertex AI Model Garden to a business use case, and learn about best practices for monitoring these models in production. We’ll go through an exercise using Colab Enterprise notebooks and learn how to use out-of-the-box tools to monitor RED (rate, error, duration) metrics, configure alerts, and monitor the rate of successful predictions in order to ensure successful use of a Vertex AI model in production.

Learn how to manage security controls and licenses for thousands of users, and tie it all together with APIs. We’ll show you ways to manage developer access more efficiently, build custom management integrations, and keep your CISO happy at the same time. We’ll also demo the new Gemini Code Assist integration with Apigee, which lets developers use Gemini Code Assist chat to generate context-aware OpenAPI specifications that reuse components from other APIs in their organization for efficiency and reference organizational security standards.

Join us for an in-depth session on Firebase Genkit, an open source framework that simplifies the development of AI-powered applications. Discover how to use the Node.js and Go SDKs to build intelligent chatbots, multimodal content generators, streamlined automation workflows, and agentive experiences. We'll demonstrate how Genkit's unified interface seamlessly integrates Google's Gemini and Imagen models, self-hosted Ollama options, and a variety of popular models from Vertex AI Model Garden. 

Migrating from AWS or Azure to Google Cloud runtimes can feel like navigating a maze of complex services and dependencies. In this session, we’ll explore key considerations for migrating legacy applications, emphasizing the “why not modernize?” approach with a practice guide. We’ll share real-world examples of successful transformations. And we’ll go beyond theory with a live product demo that showcases migration tools, and a code assessment demo powered by Gemini that demonstrates how you can understand and modernize legacy code.

Unlock the power of code execution with Gemini 2.0 Flash! This hands-on lab demonstrates how to generate and run Python code directly within the Gemini API. Learn to use this capability for tasks like solving equations, processing text, and building code-driven applications.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Personalized predictions can be created by analyzing user clickstream data and using vector embeddings to capture the essence of an entity across multiple dimensions. This establishes relationships between users and items, revealing preferences and interests. BigQuery facilitates batch processing of vector embeddings, which are then fed into Spanner for efficient retrieval of these relationships via vector search. This enables real-time personalized recommendations with sub-ms response times. This solution offers accuracy, scalability, and real-time responsiveness.

In this hands-on lab, you'll explore data with BigQuery's intuitive table explorer and data insight features, enabling you to gain valuable insights without writing SQL queries from scratch. Learn how to generate key insights from order item data, query location tables, and interact with your data seamlessly. By the end, you’ll be equipped to navigate complex datasets and uncover actionable insights quickly and efficiently.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Tired of generic code suggestions? Learn how to customize Gemini Code Assist using your source code repositories. This session covers best practices for generating new code, and retrieving and reusing existing code, with Gemini Code Assist code-customization capabilities. Boost productivity, enforce consistency, and reduce cognitive load with a truly personalized AI coding assistant.

Gemini 2.0 was built for the agentic era – from native tool use to function calling to robust support for multimodal understanding, the new frontier of applications are agentic. Join this session to explore the frontier of agents, where the best opportunities are for developers to build, open research areas to scale to billions of agents, and how to best leverage Gemini.

Did you know that GitHub Copilot lets you use Google Gemini as an AI programming assistant? Learn tips and tricks of prompting, shaping the context space, injecting third-party knowledge sources, and other ways that GitHub developers maximize their (and their team's) use of Gemini in VS Code and other IDEs.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Unleash the full potential of large language models (LLMs) on your edge devices, even when there’s spotty internet. This session explores a hybrid approach that combines the power of cloud-based LLMs with the efficiency of on-device models. Learn how to intelligently route queries, enabling laptops and mobile phones to perform complex tasks while maintaining snappy performance. View demos of efficient task routing that optimizes for quality and cost to ensure your apps run smoothly, even during network disruptions.

Many organizations are scrambling to adopt Aritificial Intelligence tools across their teams, and like any new technology rollout, they are encountering challenges- both expected and unexpected. CME Group recently rolled out Gemini Code Assist to one of their large software development organizations and are excited to share takeaways around people, process, and tools. The topics include: compliance and information security considerations, 
managing rollout and adoption: starting small and scaling, and how these tools can help reshape the workday of your teams. No matter where you are in your AI adoption journey, you're sure to learn something new!

This hands-on lab introduces Gemini 2.0 Flash, the powerful new multimodal AI model from Google DeepMind, available through the Gemini API in Vertex AI. You'll explore its significantly improved speed, performance, and quality while learning to leverage its capabilities for tasks like text and code generation, multimodal data processing, and function calling. The lab also covers advanced features such as asynchronous methods, system instructions, controlled generation, safety settings, grounding with Google Search, and token counting.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

This session explores building sensitive data protection directly into Retrieval-Augmented Generation (RAG) architectures. We'll demonstrate how to leverage Cloud Data Loss Prevention (Cloud DLP) and the Faker Library to anonymize sensitive data within the RAG pipeline. The session will cover techniques for reversible transformations using Memorystore and Firestore for data mapping, and discuss integrating these methods with Large Language Models (LLMs) like Gemini via LangChain and Vertex AI Search. Learn how to create secure and compliant AI solutions that protect sensitive data and adhere to regulations like the EU AI Act.

What if building an AI agent that thinks, reasons, and acts autonomously took less time than your coffee break? With Vertex AI, it’s not just possible. It’s easy. Join DoiT to see how to build, deploy, and scale a production-ready AI agent in 10 minutes using Google’s top services: Gemini 2 for language understanding, the RAG Engine for fetching information, and the Agent Engine for orchestration. To top it off, watch a live demo take an agent from concept to production-ready in real time.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

This session shows how engineers can use Gemini Cloud Assist and Gemini Code Assist to speed up the software development life cycle (SDLC) and improve service quality. You’ll learn how to shorten release cycles; improve delivery quality with best practices and generated code, including tests and infrastructure as code (IaC); and gain end-to-end visibility into service setup, consumption, cost, and observability. In a live demo, we’ll showcase the integrated flow and highlight code generation with GitLab and Jira integration. And we’ll show how Gemini Cloud Assist provides deeper service-quality insights.

Gemini 2.0 Flash Thinking unlocks a critical new reasoning step in the model execution process that is needed to continue hill climbing on the most difficult problems. Join Jack Rae for a deep dive on our latest thinking models, how to use them to their full capability as a developer, interesting use cases to explore with reasoning, and where we are going next with reasoning.

This session demonstrates how to integrate Gemini Code Assist into your development workflow to boost productivity and code quality. Learn specific techniques for using Gemini Code Assist and actionable strategies to incorporate it into your daily practice.

Organizations with strict data residency requirements often struggle to leverage AI and the latest in cloud innovations on-premises. Learn how to architect gen AI optimized applications for success using LLMs, cloud infrastructure, and data on-premises without compromising on data sovereignty, security, or latency in this technical deep dive session.