In this session, you’ll learn how to deploy a fully-functional Retrieval-Augmented Generation (RAG) application to Google Cloud using open-source tools and models from Ray, HuggingFace, and LangChain. You’ll learn how to augment it with your own data using Ray on Google Kubernetes Engine (GKE) and Cloud SQL’s pgvector extension, deploy any model from HuggingFace to GKE, and rapidly develop your LangChain application on Cloud Run. After the session, you’ll be able to deploy your own RAG application and customize it to your needs.

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HSBC has a large number of legacy IBM WebSphere applications that are costly to maintain and pose a compliance risk. This session will discuss how HSBC built a “migration factory” to help developers platform existing Websphere applications to Google Kubernetes Engine (GKE). The benefits of migrating these existing applications to GKE include reduced operational costs, improved compliance, increased scalability, faster application development, and improved security. Come learn exactly how HSBC did it and how you can replicate their process and success.

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Learn how platform engineering can provide multi-tenant traffic management to optimize performance, route efficiently to reduce costs, and simplify network operations. We‘ll demonstrate how multi-cluster services and multi-cluster gateways can be used to abstract the infrastructure for developers.

Learn from Shopify how they built their large-scale Kubernetes network to support 61 million shoppers and $9.3B in sales during Black Friday.

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For years now, I wanted to get my hands into Golang. The main issue was always time and a missing project fit. Gemini enabled me to learn in one evening the concepts of Golang and helped me create a small tool, that is helping in creating the Last Week In Kubernetes Development Newsletter. Explore with me what Gemini can do and how it can help you learn a new programming language by contributing to Open Source projects. Let’s also take a look at pitfalls and limits to the system and how you can work around them.

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Learn how Google Kubernetes Engine (GKE) Autopilot helped redesign Ubie's microservice platform. Ubie offers AI-based health tech products in Japan and the U.S. Since initially adopting Google Cloud six years ago, Ubie experienced growth-related challenges, particularly in reliability, security, and privacy. In this session, we delve into the strategic decision to employ GKE Autopilot in Ubie's transformation journey of re-architecture.

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If you’re a data engineer, MLOps engineer or procurement officer planning to purchase third-party AI models, you won’t want to miss this. Learn how you can speed assessment, facilitate procurement, and simplify governance of AI models (including generative AI) on Google Cloud Marketplace. Explore how to easily procure and deploy third-party AI models and frameworks to both Vertex AI and Google Kubernetes Engine. Finally, you’ll learn from Anthropic, who dive into how their solution deploys via Marketplace to Vertex AI.

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The increased adoption of Kubernetes and containerized workloads has brought about security challenges for enterprises. Malware and backdoors can pose significant risks to the underlying infrastructure in a Kubernetes cluster, potentially leading to cyber disasters. To address these challenges, there is a growing trend towards using a single tool to secure all applications running in the cloud, focusing on the shift left approach, while also securing the underlying infrastructure and assets in runtime. In this session, we’ll understand the security risks in Kubernetes and how Prisma Cloud can help solve them with a consolidated platform approach, with time for audience Q&A. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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In this talk, we delve into the complexities of building enterprise AI applications, including customization, evaluation, and inference of large language models (LLMs). We start by outlining the solution design space and presenting a comprehensive LLM evaluation methodology. Then, we review state-of-the-art LLM customization techniques, introduce NVIDIA Inference Microservice (NIM) and a suite of cloud-native NVIDIA NeMo microservices for ease of LLM deployment and operation on Google Kubernetes Engine (GKE). We conclude with a live demo, followed by practical recommendations for enterprises.

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Learn how Citadel’s fixed income fund powers their daily financial activities. First, we’ll explore the challenges of calculating profit and loss across thousands of positions, back-testing models and running trading strategies. Then we’ll discuss developing a versatile platform that bursts to thousands of workers while also handling real-time calculations. Finally, we’ll present challenges encountered and give insight on practical solutions teams can apply to their own cloud compute infrastructures.

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With Google’s new family of state-of-the-art, lightweight, and easy-to-use open models, Google Cloud is the best place to create great AI-powered experiences. In this talk, we will go over how to leverage Gemma's model's full potential with Vertex AI and Google Kubernetes Engine, including optimized performance on Google Cloud TPUs and GPUs, and show how they can easily be used and empower your team to succeed.

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Internal Developer Platforms (IDPs) are revolutionizing how engineering teams work by streamlining workflows and boosting developer productivity. But building an IDP requires a robust, scalable foundation. In this talk, we'll show you how Google Kubernetes Engine (GKE) Enterprise serves as the perfect launchpad for your IDP journey. Get ready for a hands-on demo and deep dive that will show you how GKE Enterprise simplifies IDP development with built-in security, compliance controls, and multi-cluster management.

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Learn how to optimize cloud-based file storage for various workloads. We‘ll cover Filestore and Google Cloud NetApp Volumes – fully managed Network File System and SMB solutions that balance performance, availability, and cost. We'll explore new Filestore features for modern workloads (Zonal Google Kubernetes Engine integration via the CSI Driver, protecting your data from regional failures) and how NetApp Volumes satisfies Windows workloads as well as PB scale enterprise workloads.

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Implementing generative AI applications requires large amounts of computation that can seamlessly scale to train, fine-tune, and serve the models. NVIDIA and Google Cloud have partnered to offer a range of GPU options to address this challenge. Using NVIDIA GPUs with Google Kubernetes Engine removes the heavy lifting needed to set up AI deployments, automate orchestration, manage large training clusters, and serve low-latency inference. Join us to see what ElevenLabs has built using NVIDIA GPUs with GKE. Please note: seating is limited and on a first-come, first served basis; standing areas are available

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Learn how the patent search engine company IPRally created a custom compute platform to enable higher scale data processing and deep learning. The solution relies on Ray Core and Google Kubernetes Engine, and harvests the cheapest resources from all around the world. In addition to the efficiency, the goal was to build the best environment for machine learning R&D. This has been achieved with integration to Weights&Biases as the experiment tracking system. In this session, we’ll go through on a high level the solution. Please note: seating is limited and on a first-come, first served basis; standing areas are available

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In this session, DoiT will explore the Google Kubernetes Engine (GKE) implementation of the Gateway API, and how it differs from Ingress. This talk will expand upon the advantages and future capabilities as well as how to migrate from Ingress to Gateway with ease. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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Learn about Snap's journey in developing a secure multi-tenant platform on Google Kubernetes Engine. This session dives into the elements used for service isolation in shared clusters, including container-hardening enforcements using a Kubernetes Admission Controller, identity separation using Workload Identity Federation, and access enforcements using Kubernetes Namespaces. We’ll also offer a comprehensive overview of our success, learnings, and trade-offs for building a platform that powers Snapchat's business applications.

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TwoSigma will provide an overview of its research and AI/ML Platform. The Google Kubernetes Engine-based platform seamlessly integrates with popular frameworks like Ray, Spark, and Dask allowing researchers to test investment strategies. This session will focus on the platform's architecture and capabilities and highlight a recent integration with Google Cloud's Dynamic workload Scheduler and Kueue providing researchers on-demand access to A100 and H100 graphics processing units.

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Launching a game is hard, but the pressure intensifies when your players are also fans of beloved franchises, such as Dragon Ball, Tekken, and My Hero. Delivering a perfect experience from day one requires a robust and scalable cloud infrastructure. Explore how Bandai Namco leveraged Google Cloud products like Redis, Memorystore, Google Kubernetes Engine, Spanner, and open-source games solutions to launch multiple gaming titles flawlessly. Whether you're a game developer, publisher, or platform provider, this presentation and panel discussion is about delivering high-scale consumer experiences.

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Developers choose PostgreSQL for its power, ecosystem, and enterprise-grade features. In this session, unlock best practices for building apps of all kinds with PostgreSQL. We'll cover Google Kubernetes Engine deployments, pgvector for generative AI development, performance optimization with caching, essential observability strategies, and more.

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Join iConstruye, a SaaS supply management company, as they detail their multi-phase digital transformation. They successfully migrated 135 VMs to a multi-zone Google Cloud deployment, slashing IT costs by 32%, followed by containerization on Google Kubernetes Engine, where they achieved a 25% reduction in time-to-market.

You'll gain actionable insights into their modernization strategy, including the emphasis on investing in training for their IT team on new cloud tools, reducing technical debt, and setting the stage for continued growth.

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