Session led by Paige Bailey, DevRel Lead, GenAI at Google

Learn the building blocks of autonomous agents, including core architectures, planning methods, memory systems, and leading development frameworks.

Learn the building blocks of autonomous agents, including core architectures, planning methods, memory systems, and leading development frameworks.

Learn the building blocks of autonomous agents, including core architectures, planning methods, memory systems, and leading development frameworks.

Dive into advanced reasoning, multi-agent coordination, tool chaining, self-healing workflows, and emerging security challenges.

Dive into advanced reasoning, multi-agent coordination, tool chaining, self-healing workflows, and emerging security challenges.

Dive into advanced reasoning, multi-agent coordination, tool chaining, self-healing workflows, and emerging security challenges.

Focus on real-world applications with sessions on agent evaluation, reliability, deployment strategies, and cumulative demo showcases.

Focus on real-world applications with sessions on agent evaluation, reliability, deployment strategies, and cumulative demo showcases.

Focus on real-world applications with sessions on agent evaluation, reliability, deployment strategies, and cumulative demo showcases.

Software engineering has become increasingly complex, with an ever-expanding set of patterns, frameworks, and runtimes. But help is here! AI is revolutionizing the developer workflow, and Google Cloud is reimagining the journey from idea to production. This keynote features demos that showcase how AI can streamline software engineering,  empowering you to build important apps, services, and agents faster than ever.

Software engineering has become increasingly complex, with an ever-expanding set of patterns, frameworks, and runtimes. But help is here! AI is revolutionizing the developer workflow, and Google Cloud is reimagining the journey from idea to production. This keynote features demos that showcase how AI can streamline software engineering,  empowering you to build important apps, services, and agents faster than ever.

Join us for a lightning talk summarizing the Google x Kaggle Gen AI Intensive, a 5-day live course that empowered over 140,000 participants with a comprehensive understanding of generative AI. From foundational models and prompt engineering to MLOps and real-world applications, this series covered it all through a mix of theory, hands-on learning, and community engagement, with learning material created by experts across Google. Learn how you can leverage the resources from this ongoing series to upskill yourself and stay ahead in the rapidly evolving field of generative AI.

Organizations around the world are driving change with innovative solutions, boosting efficiency, empowering employees, engaging customers, and fueling growth. Join Google Cloud CEO Thomas Kurian in our opening keynote, for exclusive insights into breakthroughs in AI and inspiring success stories from customers and partners around the world. Leave equipped to tackle real-world challenges and build for the transformative AI era.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.

We revisit the 2018 Microsoft Build in this episode, focusing on the latest ideas in DevOps. Kyle interviews Cloud Developer Advocates Damien Brady, Paige Bailey, and Donovan Brown to talk about DevOps and data science and databases. For a data scientist, what does it even mean to "build"? Packaging and deployment are things that a data scientist doesn't normally have to consider in their day-to-day work. The process of making an AI app is usually divided into two streams of work: data scientists building machine learning models and app developers building the application for end users to consume. DevOps includes all the parties involved in getting the application deployed and maintained and thinking about all the phases that follow and precede their part of the end solution. So what does DevOps mean for data science? Why should you adopt DevOps best practices? In the first half, Paige and Damian share their views on what DevOps for data science would look like and how it can be introduced to provide continuous integration, delivery, and deployment of data science models. In the second half, Donovan and Damian talk about the DevOps life cycle of putting a database under version control and carrying out deployments through a release pipeline.