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!

Build a multimodal search engine with Gemini and Vertex AI. This hands-on lab demonstrates Retrieval Augmented Generation (RAG) to query documents containing text and images. Learn to extract metadata, generate embeddings, and search using text or image queries.

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!

Gemini 2.0, the latest foundational model released by Google DeepMind, offers improved performance, real-time interactions support, text-to-image and text-to-audio generations, Google Search grounding, and reasoning – all under a unified SDK that allows you to flawlessly navigate from the Gemini API to Vertex AI. In this talk, you’ll learn about the newest Gemini 2.0 capabilities, how to accelerate your prototyping, and guidelines to deploy your solutions from a single API to more complex pipelines.

Learn how leading startups build transformative AI solutions with Vertex AI. Gain behind-the-scenes insights, practical strategies, and actionable takeaways to fuel your innovation using Google Cloud’s open ecosystem of models and APIs.

Deliver, protect, and scale immersive web, media, generative AI and API experiences with the Google Cloud global frontend as part of our Cross-Cloud Network for backend infrastructure running in any cloud or on-premises. Learn about new innovations providing consistent global performance to improve user experience; advanced protection to help stop DDoS, web, API, and gen AI attacks; scalable traffic control for traffic spikes and scale; edge programmability to allow customizations and integrations; and automation to enable end-to-end deployment automation.

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!

Learn how to simplify cloud migrations with real-world lessons from moving a client from AWS Batch to Google Cloud Batch. This session will cover key areas like translating batch job definitions, optimizing workflows for better performance, and integrating applications with the Google Cloud Batch API. Along the way, we’ll explore strategies to address client concerns and ensure a smooth transition. Whether you’re planning a similar move or just curious about the behind-the-scenes, this session will give you some practical insights.

This hands-on lab guides you through building a captivating generative AI application using the Gemini API in Vertex AI. You'll leverage the Streamlit framework to create an interactive interface for generating stories, providing a seamless user experience. After testing your application locally in Cloud Shell, you'll deploy it to Cloud Run for scalable and reliable serving. This hands-on experience equips you with the skills to integrate Gemini with user interfaces and efficiently deploy your AI 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!

Google is transforming its marketing workflows with AI, slashing task time from weeks to minutes. This session explores the business problem and the solution: a Gemini-powered product that embeds AI-assisted content creation directly into our publishing platform replacing over 10 disparate tools. We will share some key technical considerations when working with Gemini including API design, prompt engineering versus fine-tuning, and evaluation techniques to ensure high quality output. Learn how to achieve similar results by integrating AI into your marketing ecosystem.

As the number of distributed and generative AI applications grows in your organization, so will the number of APIs – presenting management and governance challenges. This session introduces API Hub, a solution for unified API management. Learn how to gain centralized observability over your APIs, use the API Hub on-ramp framework to connect to any API gateway, help developers access shared components and standards to build compliant APIs faster, and discover and manage shadow APIs.

This talk delves into the difficulties of managing AI costs, emphasizing cost estimation across various model deployments (closed foundation models, non-cloud provider APIs, and local models). Key areas of focus include strategies for projecting AI spend at both the workload and enterprise level, implementing cost showback mechanisms, optimizing AI spending, and developing business plans and forecasts using ML and GenAI.

Transform your AI research into real-world applications with Google’s latest tools. This session explores the seamless integration of the Gemini API, Google AI Studio, Gemma, and Kaggle to accelerate your development workflow. Learn how to build and prototype models effortlessly, leverage lightweight open models, and collaborate with a thriving community. Discover how to deploy your experiments in production using Cloud Run and Vertex AI. Join us to bridge the gap between research and reality with Google AI.

This hands-on lab equips you with the practical skills to build and deploy a real-world AI-powered chat application leveraging the Gemini LLM APIs. You'll learn to containerize your application using Cloud Build, deploy it seamlessly to Cloud Run, and explore how to interact with the Gemini LLM to generate insightful responses. This hands-on experience will provide you with a solid foundation for developing engaging and interactive conversational 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!

Google Cloud’s Sensitive Data Protection service is a highly effective capability that can discover and classify sensitive data in your environment, helping to prevent data leakage. But it also has features useful to developers to minimize the exposure of confidential customer information when handling large volumes of sensitive data. By taking advantage of Sensitive Data Protection transformation techniques, you can de-identify sensitive information in a dataset through redaction, replacement, masking, tokenization, bucketing, date shifting, and time extraction. Developers retain the ability to test applications using functional data while still meeting security requirements put in place to protect customer information. By using pseudonymization, which is reversible and provides an easier path for troubleshooting, developers will have a more useful dataset for functional testing than they would if they used data anonymization. In this talk, you’ll learn how to use the Cloud Data Loss Prevention API (DLP API) of Sensitive Data Protection to inspect data for sensitive information and build an automated data transformation pipeline to create de-identified copies of your dataset.

This hands-on lab equips you with the practical skills to build and deploy a real-world AI-powered chat application leveraging the Gemini LLM APIs. You'll learn to containerize your application using Cloud Build, deploy it seamlessly to Cloud Run, and explore how to interact with the Gemini LLM to generate insightful responses. This hands-on experience will provide you with a solid foundation for developing engaging and interactive conversational 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!

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!

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!

Discover and learn about the service approach, which mean to expose capabilities through API for a company using Generative AI for content creation. 

Orchestration, scalability and micro services approach will be exposed during this session, to show how you can industrialize at scale the services to serve millions of users.

Build a multimodal search engine with Gemini and Vertex AI. This hands-on lab demonstrates Retrieval Augmented Generation (RAG) to query documents containing text and images. Learn to extract metadata, generate embeddings, and search using text or image queries.

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!

Redpanda, a leading Kafka API-compatible streaming platform, now supports storing topics in Apache Iceberg, seamlessly fusing low-latency streaming with data lakehouses using BigQuery and BigLake in GCP. Iceberg Topics eliminate complex & inefficient ETL between streams and tables, making real-time data instantly accessible for analysis in BigQuery This push-button integration eliminates the need for costly connectors or custom pipelines, enabling both simple and sophisticated SQL queries across streams and other datasets. By combining Redpanda and Iceberg, GCP customers gain a secure, scalable, and cost-effective solution that transforms their agility while reducing infrastructure and human capital costs.

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