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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Migrating high performance computing (HPC) workloads to the cloud presents unique challenges, as traditional on-premises infrastructure often clashes with cloud architectures, leading to operational and cost inefficiencies. Embracing core technologies like Google Kubernetes Engine and Google Cloud Storage offers a compelling solution to these hurdles. In this session, we explore PGS the transition of our entire HPC system to Google Cloud. This move allows us to run workloads five times larger than previously possible while reducing turnaround time by a factor of two.

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As machine learning (ML) systems continue to evolve, the ability to scale complex ML workloads becomes crucial. Scalability can be considered along two dimensions: expansive training of large language models (LLMs) and intricate distribution of reinforcement learning (RL) systems. Each has its own set of challenges, from computational demands of LLMs to complex synchronization in distributed RL.

This session explores the integration of Ray, Google Kubernetes Engine (GKE) and ML accelerators like tensor processing units (TPUs) as a powerful combination to develop advanced ML systems at scale. We discuss Ray and its scalable APIs, its mature integration with GKE and ML accelerators, and demonstrate how it has been used for LLMs and re-implementing the powerful RL algorithm, Muzero.

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This session features panel discussion with Snap Inc., and its journey from being born on Google App Engine to how they’ve been able to grow and serve 400M+ DAU powered by Google Kubernetes Engine. Learn about the business decisions behind this evolution, as we dive into the strategic approach delivered by Snap’s leadership throughout the company’s history as a digital-born customer.

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If you are curious how to accelerate developers innovation, inner sourcing and governance by taking Crème de la crème from Google Cloud developer toolset and open source that session is for you. Leverage best of OSS and GCP to make it easy. During presentation you will learn how to accelerate application and infrastructure delivery from Google Cloud in use of Kubernetes Resource Model, empowered by GKE Enterprise and Cloud Deploy and exposed to developers via OSS Backstage Portal. All ended with practical use case demo.

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Cloud-native applications can be complex, but securing them shouldn’t be. Learn how CrowdStrike Falcon Cloud Security enables DevOps and SecOps to discover weak spots in their container images, prevent malicious behavior on Kubernetes clusters, visualize sensitive data flows, and discover misconfigurations across all of their cloud accounts. This session is for anyone responsible for application or cloud security. 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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Deploying AI to production can be bafflingly complex. Learn how Google Cloud is bringing its over two decades of expertise in productionizing planet scale AI to our cloud customers with the AI Hypercomputer architecture. It’s a groundbreaking supercomputing architecture built on performance-optimized hardware (TPUs, GPUs), open software (PyTorch, Jax, Kubernetes), and tailored consumption models that optimize efficiency and productivity across AI training, tuning, and serving. Plus, gain valuable insights from our customers Kakao Brain and Nuro on their journey to deploying large scale AI on Google Cloud.

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In this session, you will learn how Rent the Runway (RTR) relies on MongoDB Atlas on Google Cloud to mix their automation hardware with their software, needing a robust, flexible, and intuitive data platform. We’ll dive into some reference architecture, highlighting some key integrations, such as Google Kubernetes Engine. We will then discuss RTR’s AI strategy, discussing how they’re approaching AI tools for their products. Lastly, we’ll discuss RTR and MongoDB’s mission of sustainability. Q&A to follow.

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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Introducing Google Kubernetes Engine (GKE) Threat Detection powered by Security Command Center (SCC). Event Threat Detection protects your use of Google Cloud from the Identity layer up through Network layer detections. Discover how GKE and SCC deliver a better-together integrated experience to detect threats against the container infrastructure.

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Large Language Models (LLMs) have changed the way we interact with information. A base LLM is only aware of the information it was trained on. Retrieval augmented generation (RAG) can address this issue by providing context of additional data sources. In this session, we’ll build a RAG-based LLM application that incorporates external data sources to augment an OSS LLM. We’ll show how to scale the workload with distributed kubernetes compute, and showcase a chatbot agent that gives factual answers.

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Worried about compliance for your platform and containers? Google Kubernetes Engine (GKE) has you covered. This session unlocks the power of GKE Compliance Posture, your real-time dashboard for proactive risk detection and continuous compliance. You’ll be able to see your entire GKE compliance landscape at a glance; stay ahead of risks with constant monitoring against industry standards; and get clear guidance to fix gaps and boost security. Plus, learn from SADA customers.

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