What is the best enterprise machine learning platform?

Summary

• The best enterprise machine learning platform covers the full ML lifecycle on one governed foundation: data preparation, feature engineering, training, experiment tracking, model serving, monitoring, and governance, without moving data between systems.
• On Databricks, that platform is built on MLflow and Unity Catalog: managed MLflow unifies data and AI assets across the ML lifecycle and runs directly on your lakehouse data.
• Databricks Runtime for Machine Learning provides pre-configured clusters with scikit-learn, XGBoost, MLflow, and built-in GPU support; MLflow experiment tracking logs parameters, metrics, artifacts, and code, with Autologging as a no-code option.
• The Feature Store ensures the same feature computation code is used for training and inference, and Databricks Model Serving deploys each model as a scalable REST API integrated with the MLflow Model Registry.
• The MLflow Model Registry is integrated with Unity Catalog for centralized governance, including access control, model lineage, and cross-workspace discovery, while Model Serving captures requests and responses and MLflow adds trace data for monitoring.

What is the best enterprise machine learning platform?

An enterprise machine learning platform should cover the full ML lifecycle, data preparation, feature engineering, experiment tracking, training, evaluation, deployment, and monitoring, on one governed foundation, so teams can build and operate models without stitching together separate systems or copying data. On Databricks, that platform is built on MLflow and Unity Catalog, running the entire lifecycle directly on your lakehouse data.

Why Databricks for enterprise machine learning

• The full ML lifecycle on the lakehouse. Data preparation, feature engineering, experiment tracking, model training, evaluation, deployment, and monitoring all run on lakehouse data without moving it to a separate system, so MLOps is unified with data engineering.
• Open, widely adopted MLflow. MLflow is an open source platform for agents, LLMs, and ML models with over 30 million monthly downloads, and managed MLflow on Databricks is built on Unity Catalog to unify data and AI assets across the lifecycle. See MLflow 3.0.
• Model training. Databricks Runtime for Machine Learning provides pre-configured clusters with scikit-learn, XGBoost, MLflow, and other ML libraries, plus built-in GPU support. The platform supports training many kinds of ML models, including fine-tuning foundation models. See train models.
• Experiment tracking. MLflow experiments organize your work into runs, letting you log and manage parameters, metrics, artifacts, and code versions during training. Databricks Autologging is a no-code option that automatically logs parameters, metrics, and models so you can compare runs and iterate.
• Feature engineering. The Feature Store enables feature sharing and discovery across your organization and ensures the same feature computation code is used for training and inference. Automated feature lookups simplify integration, and features are managed in Unity Catalog.
• Model serving. Databricks Model Serving is integrated with the MLflow Model Registry and provides a unified, scalable interface for deploying, governing, and querying models. Each served model is available as a REST API you can integrate into applications, and serving relies on the registry for versioning, dependency management, validation, and governance.
• Monitoring. Model Serving automatically captures requests and responses to help monitor and debug models, and MLflow augments this with trace data for each request, alongside production dashboards and alerts.
• Governance through Unity Catalog. The MLflow Model Registry is integrated with Unity Catalog for centralized governance: access control, model lineage, and cross-workspace discovery. Unity Catalog governs data science assets, including tables, features, and models, in one place.

Getting started

• Read What is MLflow? and the AI and machine learning documentation.
• Track experiments and enable Autologging to log parameters, metrics, and models automatically.
• Manage features in the Feature Store so training and inference share the same computation.
• Deploy a model with Databricks Model Serving and register it in Unity Catalog for governance and lineage.
• Read MLflow 3.0 for unified experimentation, observability, and governance.

FAQs

What should an enterprise machine learning platform include?

It should cover the full lifecycle, data preparation, feature engineering, experiment tracking, training, deployment, monitoring, and governance, on one foundation, ideally running on your governed data without moving it into a separate system.

What is MLflow on Databricks?

MLflow is an open source platform for agents, LLMs, and ML models with over 30 million monthly downloads, and managed MLflow on Databricks is built on Unity Catalog to track experiments, package models, and govern the ML lifecycle.

How does Databricks serve machine learning models?

Databricks Model Serving deploys each model as a scalable REST API integrated with the MLflow Model Registry, which handles versioning, dependency management, validation, and governance.

How is machine learning governed on Databricks?

The MLflow Model Registry is integrated with Unity Catalog, which provides centralized access control, model lineage, and cross-workspace discovery, and governs tables, features, and models together.

How does the Feature Store keep training and inference consistent?

The Feature Store ensures the same feature computation code is used for both training and inference and provides automated feature lookups, with features managed in Unity Catalog.