Databricks Model Serving
Deploy and govern models at scale.
Overview
Databricks Model Serving provides a highly available and low-latency service for deploying machine learning models. The service automatically scales up or down to meet demand changes, saving infrastructure costs while optimizing latency performance. It is deeply integrated with the Databricks Lakehouse Platform, allowing for seamless deployment of models trained on Databricks.
✨ Key Features
- Serverless real-time inference
- Automatic scaling
- High availability and low latency
- Integrated with MLflow Model Registry
- Support for custom models and foundation models
- Built-in monitoring and governance
🎯 Key Differentiators
- Deep integration with the Databricks Lakehouse Platform
- Unified governance and monitoring across data and AI
- Serverless and auto-scaling for cost-effective inference
Unique Value: Provides a fully managed and integrated solution for deploying, serving, and governing machine learning models at scale within the Databricks Lakehouse Platform.
🎯 Use Cases (4)
✅ Best For
- Personalized recommendations
- Fraud detection
- Customer lifetime value prediction
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Organizations that are not using the Databricks platform for their data and AI workloads.
🏆 Alternatives
Offers a more seamless and governed experience for deploying models trained on Databricks compared to using external deployment solutions, and a more data-centric approach than general-purpose cloud ML platforms.
💻 Platforms
🔌 Integrations
🛟 Support Options
- ✓ Email Support
- ✓ Live Chat
- ✓ Phone Support
- ✓ Dedicated Support (Premium tier)
🔒 Compliance & Security
💰 Pricing
✓ 14-day free trial
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