Powering Scalable Analytics and AI with Azure Data Lake Storage

Conventional AI models predict outcomes by analyzing data, while Generative AI (Gen AI) and large language models (LLMs) generate entirely new outputs. Scaling model building and inferencing pose challenges in maintaining precision, governance, and security for customer-facing applications. Azure Storage offers scalable, secure cloud storage that integrates with industry leading analytics engines such as Azure Databricks and Microsoft Fabric to provide a unified analytics platform for data engineering, data science, and machine learning.

Join us for an in-depth session, we'll delve into the underlying storage architecture that enables hyper-scale workloads to utilize Azure Data Lake Storage (ADLS) with analytics engines of their choice for tasks like data cleansing and curation within Spark pipelines for Big Data Analytics. You will discover how this curated data can then be accessed by data science and business intelligence teams and can be provided to machine learning teams for further training of GPT-X models. We will showcase architectural patterns, real-world workload performance, and storage behavioral characteristics of workloads running at scale on Azure Storage today.