This session explores the evolution of data management on Kubernetes for AI and machine learning (ML) workloads and modern databases, including Google’s leadership in this space. We’ll discuss key challenges and solutions, including persistent storage with solutions like checkpointing and Cloud Storage FUSE, and accelerating data access with caching. Customers Qdrant and Codeway will share how they’ve successfully leveraged these technologies to improve their AI, ML, and database performance on Google Kubernetes Engine (GKE).

Discover how to transition from legacy, siloed systems to a unified, scalable, and insights-driven data platform on GCP. This session will cover best practices for data migration, overcoming common challenges, and integrating SaaS and third-party solutions using key Google Cloud services like BigQuery, Data Fusion, Cloud Storage, Application Integration, Cloud Run, Cloud Build, and Artifact Registry.

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Introduce how easy it can be to build a fully function Flutter app with Firebase Backend. It's fast, easy, and fully function with Front-end UI, and backend task (using Cloud Function), database (Firestore), storage (Cloud Storage), and more. Additional tips on Vertex AI and Gemini in both Flutter and Cloud Function will be added if time allows.

Modern analytics and AI workloads demand a unified storage layer for structured and unstructured data. Learn how Cloud Storage simplifies building data lakes based on Apache Iceberg. We’ll discuss storage best practices and new capabilities that enable high performance and cost efficiency. We’ll also guide you through real-world examples, including Iceberg data lakes with BigQuery or third-party solutions, data preparation for AI pipelines with Dataproc and Apache Spark, and how customers have built unified analytics and AI solutions on Cloud Storage.

Discover the latest breakthroughs in Cloud Storage. This executive session provides a high-level overview of the latest object, block, file storage, and backup and recovery solutions. Gain insights into our cutting-edge storage technologies and learn how they can optimize your infrastructure, reduce costs, and enhance your data management strategy. Don’t miss this opportunity to learn directly from Google executives about the future of storage. This session is a must for IT decision-makers seeking a competitive edge.

This talk offers demonstrations and live discussions on how to rapidly deploy production-ready GKE or Slurm clusters using Cluster Toolkit and Terraform. Leverage the latest GPUs to accelerate machine learning workloads and optimize resource utilization with GKE's Kueue, autoscaling Slurm, and Dynamic Workload Scheduler (DWS). Explore storage solutions like Google Cloud Storage (GCS), GCSFuse, Filestore Zonal, and Parallelstore. Leave this session with the tools and knowledge you need to deploy a high-performance ML cluster in minutes.

Properly architecting your storage infrastructure for AI is critical for success. Snap will share some of their best practices, implementation tips, and success stories for AI workloads. This session dives deep into training, checkpointing, and serving recommendations, covering Cloud Storage FUSE, Anywhere Cache, and parallel file systems. Gain insights to optimize your AI infrastructure and unlock its full potential. Don’t miss this opportunity to learn from real-world examples and expert advice.

Managing petabytes of Google Cloud storage objects? Attend this session to learn how the new Storage Intelligence product simplifies managing billions of objects across thousands of buckets. Leverage AI-driven insights to analyze cost, performance, security, and compliance – all through intuitive natural language queries. Quickly act on insights with bucket relocation and batch operations. Join us to uncover practical tools and exciting new features that can transform you into a Cloud Storage superhero.

Unlock the Power of Fine-Tuning with Apps Script! Learn how to optimize pre-trained models for specific tasks using Google Apps Script. This session covers exporting data from Sheets to Cloud Storage as JSONL, building an Apps Script prompt explainer backend, and creating service accounts for secure access to Vertex AI and Cloud Storage. We'll also show how to collect, transform, and split data for training, launch the fine-tuning process, and test results in Vertex AI and a Google Chat bot. Master fine-tuning for practical AI applications.

Achieve high-performance AI training and serving with Cloud Storage. Use Gemini Cloud Assist for object-level granular storage analysis with Storage Intelligence.