Fine-tune LLM with Databricks Unity Catalog and Amazon SageMaker AI

2026-05-13 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Cybersecurity & Data Privacy · Depth: Advanced, long

Summary

A secure, complete workflow for fine-tuning large language models (LLMs) integrates Databricks Unity Catalog with Amazon SageMaker AI, using Amazon EMR Serverless for preprocessing. This solution addresses challenges in maintaining strict data governance and lineage when using best-in-class machine learning services. The process involves reading training data from a Unity Catalog-managed table, preprocessing it with EMR Serverless and Apache Spark, fine-tuning a Ministral-3-3B-Instruct model using SageMaker AI Training jobs, and tracking data lineage in Unity Catalog from source data to the trained model. This integration ensures consistent policy enforcement, auditability, and compliance, particularly crucial for regulated industries and production workloads, by preventing SageMaker AI Training jobs from bypassing Unity Catalog's fine-grained authorization model.

Key takeaway

For MLOps Engineers building generative AI solutions in regulated industries, integrating Databricks Unity Catalog with Amazon SageMaker AI is critical for maintaining data governance and audit trails. This approach ensures that LLM fine-tuning workflows comply with security requirements by preserving fine-grained authorization and tracking end-to-end data lineage. You should adopt this pattern to securely manage your ML models and data, leveraging Unity Catalog's capabilities for centralized control across AWS services.

Key insights

Integrate Databricks Unity Catalog with AWS ML services for governed LLM fine-tuning and end-to-end data lineage.

Principles

• Centralize data governance with Unity Catalog.
• Maintain data lineage across disparate services.
• Secure programmatic access using OAuth M2M.

Method

The workflow reads Unity Catalog-governed data, preprocesses it with EMR Serverless, fine-tunes a Ministral-3-3B-Instruct model via SageMaker AI, and registers artifacts and external lineage back into Unity Catalog.

In practice

• Use 8-bit quantization and LoRA for efficient LLM fine-tuning.
• Store Databricks OAuth credentials in AWS Secrets Manager.
• Configure EMR Serverless with internet access for Delta Lake JARs.

Topics

• Databricks Unity Catalog
• Amazon SageMaker AI
• LLM Fine-tuning
• Data Governance
• Data Lineage

Code references

• aws-samples/sample-fine-tune-LLM-with-Databricks-Unity-Catalog-and-Amazon-SageMaker-AI

Best for: Machine Learning Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.