Fine-tune LLM with Databricks Unity Catalog and Amazon SageMaker AI | Amazon Web Services
When you fine-tune large language models (LLMs) with Amazon SageMaker AI while using Databricks Unity Catalog, you might face unique challenges like how to maintain strict data governance while using best-in-class machine learning (ML) services.
Unity Catalog governs metadata and permissions, while the underlying data resides in Amazon Simple Storage Service (Amazon S3) when you choose AWS as the cloud environment for their Databricks Workspace. When SageMaker AI Training job accesses that data, you must preserve and not bypass the Unity Catalog’s fine-grained authorization model. Without a structured integration pattern, you risk inconsistent policy enforcement, audit gaps, and compliance exposure. For example, if SageMaker AI Training jobs bypass Unity Catalog’s authorization model when reading S3 objects, you lose visibility into which data trained which models. This creates critical compliance risks particularly in regulated industries and production workloads.
In this post, we demonstrate how to build a secure, complete LLM...
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