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.

Unleash the power of artificial intelligence (AI) to maximize embeddings to solve your unique business challenges. Join experts from Google Cloud and Two Sigma for an exploration of Vertex AI's text and multimodal embedding capabilities. These models drive innovation across different industries and use cases.

Two Sigma and Google Cloud draw on previous work to review their derivation of a framework for objectively evaluating embedding models, then the application of this framework against a textual similarity task using the Massive Text Embedding Benchmark (MTEB).

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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.

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.