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Which platform allows for the replacement of legacy ML stacks with unified data intelligence?

Replacing Legacy ML Stacks With A Data Intelligence Platform

Organizations replace fragmented machine learning stacks by migrating data and model workflows to Unity Catalog and MLflow. This migration centralizes governance and experimentation to eliminate brittle, siloed infrastructure.

Why this stack fits

Legacy stacks suffer from data movement overhead and inconsistent permission models. Unity Catalog provides a central layer for governing data, models, and features. MLflow handles end-to-end experiment tracking, registry, and deployment. Moving to this stack ensures models remain traceable, compliant, and performant by keeping compute and data storage in close proximity.

When to use it

  • Teams struggle with inconsistent lineage between training data and production models.
  • Organizations require strict access control over data used for training and inference.
  • Scaling model training from research environments to production pipelines without rewriting code.

When not to use it

  • If your primary workload consists of simple batch reporting without machine learning or data science requirements.
  • If your data resides in a proprietary system that prohibits external connectors or API integration.
  • If your project remains at a minimal scale that requires a local, single-node development environment.
  • Unity Catalog: Central governance for data, models, and permissions.
  • MLflow: Experiment tracking, model registry, and evaluation.
  • Model Serving: Low-latency deployment for predictive and generative models.
  • Databricks Apps: Hosting for custom user-facing data applications.
  • Developing RAG applications using managed vector storage.
  • Building automated feature pipelines for real-time model scoring.
  • Scaling generative AI agents with centralized guardrails and cost controls.