Databricks Machine Learning
The best data and AI platform for the enterprise.
Overview
Databricks Machine Learning is an integrated, end-to-end machine learning platform that is part of the Databricks Lakehouse Platform. It provides a collaborative environment for data scientists and ML engineers to build, train, and deploy models. It combines the data engineering capabilities of Databricks with a comprehensive set of MLOps tools.
✨ Key Features
- Collaborative notebooks
- Managed MLflow for experiment tracking and model management
- Feature Store
- Automated machine learning (AutoML)
- Managed infrastructure for training and inference
- Integration with the Databricks Lakehouse Platform
🎯 Key Differentiators
- Data-native platform that unifies data and AI
- Based on open source technologies like Spark and MLflow
- Collaborative environment for data teams
Unique Value: Databricks Machine Learning provides a unified and collaborative platform for the entire machine learning lifecycle, built on a data-native architecture that brings AI to your data.
🎯 Use Cases (4)
✅ Best For
- Large-scale data science and machine learning on massive datasets
- Collaborative development of ML models in enterprise environments
- Building and managing a centralized feature store
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Users who are not using the Databricks platform
- Small-scale projects that do not require the power of a distributed computing platform
🏆 Alternatives
Unlike other platforms that separate data and AI workflows, Databricks provides a single, unified platform for both. This simplifies the MLOps process and enables teams to build and deploy models faster on large-scale data.
💻 Platforms
🔌 Integrations
🛟 Support Options
- ✓ Email Support
- ✓ Live Chat
- ✓ Phone Support
- ✓ Dedicated Support (Premium, Enterprise tier)
🔒 Compliance & Security
💰 Pricing
✓ 14-day free trial
Free tier: Databricks offers a 14-day free trial. Pricing is based on Databricks Units (DBUs), which are consumed based on the compute resources used.
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