Today's applications are built using containers, and Kubernetes has become the de facto standard for running containerized workloads. Google Kubernetes Engine (GKE) is the most automated, scalable, and fully managed Kubernetes platform run by the largest contributor to Kubernetes. Join this session to learn how GKE simplifies compute orchestration, provides cost-optimized autoscaling and reduces Day 2 operations, allowing you to focus on building and running your application workloads while Google Cloud manages your infrastructure.

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Web Security Scanner identifies security vulnerabilities in your App Engine, Google Kubernetes Engine (GKE), and Compute Engine web applications. This service crawls your application, following all links within the scope of your starting URLs, and attempts to exercise as many user inputs and event handlers as possible. It can automatically scan and detect four common vulnerabilities, including cross-site-scripting (XSS), flash injection, mixed content (HTTP in HTTPS), and outdated/insecure libraries. In this spotlight lab, you will use Web Security Scanner—one of Security Command Center's built-in services—to scan a Python Flask application for vulnerabilities.

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Join this session to discover how Unity, one of the world's top video game software developer, runs their deployment on Google Kubernetes Engine (GKE) on Google Cloud. In this fireside chat, we will go through this evolution journey together highlighting key milestones and best practices so that you too can run your enterprise deployment on GKE.

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X509 certificates and Google Cloud Certificate Authority Service offer powerful authentication solutions for workloads, micro-services, and smart devices. This session will guide you through the latest innovations in certificate provisioning and management for VMs, Kubernetes, and Load Balancers. We'll delve into SPIFFE, mTLS, and service-to-service authentication techniques. Gain real-world insights from Signify (formerly Philips Lighting) and see how they've implemented Google Cloud Certificate Authority Service and the Matter standard within their IoT ecosystem.

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Modernizing high-performance and AI workloads that need to stay on-premises can be challenging. Learn from the experts how to optimize your hybrid cloud by deploying Google Kubernetes Engine (GKE) on Google Distributed Cloud. Deliver a consistent developer experience on-premise and in the cloud, and preserve your return on investments by leveraging your existing HW investments. Discover how to optimize upgrade cycles, streamline operations, and empower your developers with the same cloud-born tools they use with GKE.

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Come to this fireside chat with Seth Vargo to learn more about the ultimate hybrid cloud use case. We'll explore use cases where Alphabet products run on some of your favorite Google Cloud offerings such as Google Kubernetes Engine (GKE). Why don't we run everything at Alphabet on Google Cloud? Why do some products run partially on cloud? How do Alphabet engineers take advantage of products like GKE, Cloud Run, Vertex AI, and Gemini exposed over hybrid channels? We'll shed light on how our internal innovation influences the products available to our customers and vice-versa.

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Ready to level up your infrastructure, Google Kubernetes Engine, and networking skills with the power of Gemini? Join this session to learn how large language models work and how it applies to roles in infrastructure, DevOps, and networking.

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As generative AI applications mature, retrieval-augmented generation (RAG) has become popular for improving large language model-based apps. We expect teams to move beyond basic RAG to autonomous agents and generative loops. We'll set up a Weaviate vector database on Google Kubernetes Engine (GKE) and Gemini to showcase generative feedback loops.

After this session, a Google Cloud GKE user should be able to: - Deploy Weaviate open source on GKE - Set up a pipeline to ingest data from the Cloud Storage bucket - Query, RAG, and enhance the responses

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This session is for AI/ML and data practitioners who want to build AI/ML data pipelines at scale and select the right combination of block, file, and object storage solution for your use case. Learn how to optimize all your AI/ML workloads like data preparation, training, tuning, inference, and serving with the best storage solution and easily integrate them into your Compute Engine, Google Kubernetes Engine, or Vertex workflows. We’ll also dive into how to optimize analytics workloads with Cloud Storage and Anywhere Cache.

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Embark on a journey through the diverse landscape of website deployment options available on Google Cloud. In this session, we’ll guide you through various ways to deploy a website, offering insights into the array of tools and services Google Cloud provides for hosting, scaling, and optimizing web applications. From traditional virtual machine instances to containerized solutions with Kubernetes, and serverless deployment with Cloud Functions and Cloud Run, this session will cover the entire spectrum of deployment strategies.

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Kubernetes is the de facto standard for running modern, containerized workloads in cloud. Google open-sourced Kubernetes 10 years ago and has built the easiest to use and most scalable managed service in Google Kubernetes Engine (GKE). Join this keynote to learn how top customers are leveraging GKE to run their businesses and to hear about the latest innovations and future directions of GKE.

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Discover modern cloud migration strategies. Learn to leverage Google Cloud for cost optimization and easy transitions from on-premise infrastructure or competing cloud providers. Get real demos of top migration methods to Google Cloud environments like, Google Cloud VMware Engine, Google Compute Engine, Google Kubernetes Engine, and Cloud Run. Learn to migrate an application across Google Cloud infrastructure levels, fine-tuning for cost and performance. Plus, get insights from Global Payments, with a real-world Google Cloud migration success story.

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Attend this session for an overview of our storage solutions and how they are optimized for a variety of workloads. We’ll share storage best practices for AI, Google Kubernetes Engine, and VMs, and customer storage and infrastructure cost optimization. You’ll gain insight into new features that deliver more performant and available apps to your business. We’ll also share our storage vision providing you the ability to plan for future application workloads.

-Industry’s first unified cloud storage optimized for AI and analytics workloads; -Intelligent storage powered by AI; -Built for mission-critical, high-availability data protection; -Migrating to cloud storage and Google Cloud at scale at low cost

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Google Kubernetes Engine (GKE) provides cost efficiency and high performance to run AI inference on Google tensor processing units (TPUs) and NVIDIA graphics processing units. Join us to learn how Anthropic runs its inference workload for Claude on GKE, and how Anthropic achieved better price-perf on TPU v5e on GKE. We’ll also learn how GKE advanced management capabilities simplify Day-2 maintenance, and how Google Cloud Customer Support makes the entire experience a blast.

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The State of Kubernetes Cost Optimization report (https://goo.gle/state-of-kubernetes-cost-optimization) found that more than expected Kubernetes Pods aren’t setting accurate resource requests and limits. This can lead to workloads being abruptly terminated, and OSS Kubernetes doesn't adequately track those terminations. We invite you to this talk to learn how Google Kubernetes Engine and Google Cloud Logging comes to the rescue.

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Ditch legacy and embrace freedom with AlloyDB Omni, your hybrid and multicloud enterprise database. Run anywhere, from data centers to the public clouds of your choice, and unlock performance and ease of management. Elevate your apps with HTAP and built-in generative AI to build vector embeddings for lightning-fast search, remotely or locally – no connectivity needed. Simplify operations with the Kubernetes operator: automate lifecycle, HA/DR, and scale effortlessly. Learn more about AlloyDB Omni and supercharge your data strategy, anywhere.

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The number of clusters running data apps on Google Kubernetes Engine has grown exponentially, doubling every year since 2019. With the rise of AI/ML along with accelerated compute, data architectures are gaining importance. Join this session to learn about Kubernetes data architectures for AI/ML, storage best practices, data availability and customer use cases. This session is meant to educate you about retooling your skill set for the new paradigm of data on Kubernetes.

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Learn how Google Cloud’s backup and storage services secure and protect your data from a variety of threats, such as ransomware, outages, and user errors. Our backup services protect VMs, databases (such as SAP HANA), and Google Kubernetes Engine environments. Expand threat detection capabilities by alerting on suspicious activities around backup through Security Command Center. We’ll also dive into Cloud Storage and our industry-leading turbo replication for dual-region deployments, soft delete, versioning, and more to protect your data.

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In this session, you'll learn how the platform team can provide a multi-tenant traffic-management infrastructure to optimize performance, route efficiently to reduce costs, and simplify operations. We'll demonstrate how multi-cluster service and multi-cluster Gateway can be used to abstract the cloud infrastructure and how Google Kubernetes Engine (GKE) Enterprise can empower the platform team managing fleets of GKE clusters and teams consuming those clusters.

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Text-to-image generative AI models such as the Stable Diffusion family of models are rapidly growing in popularity. In this session, we explain how to optimize every layer of your serving architecture – including TPU accelerators, orchestration, model server, and ML framework – to gain significant improvements in performance and cost effectiveness. We introduce many new innovations in Google Kubernetes Engine that improve the cost effectiveness of AI inference, and we provide a deep dive into MaxDiffusion, a brand new library for deploying scalable stable diffusion workloads on TPUs.

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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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