Description
Databricks Model Serving provides a comprehensive solution for enterprises looking to deploy classical machine learning models, generative AI models, and AI agents. This platform supports both proprietary models, such as Azure OpenAI and AWS Bedrock, as well as open-source models like Llama and Mistral. Users can serve fine-tuned open-source models or classical ML models trained on their own data, allowing for flexibility in model deployment. The served models can be easily utilized as endpoints in various workflows, including large-scale batch inference and real-time applications.
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One of the key features of Databricks Model Serving is its built-in governance, lineage, and monitoring capabilities, which ensure high-quality outputs. This is particularly important for organizations that require strict compliance and oversight of their AI models. The platform simplifies the deployment process for all types of AI models and agents, enabling users to deploy pretrained open-source models or custom models built on their own data, whether on CPUs or GPUs. Automated container building and infrastructure management reduce maintenance costs and accelerate deployment, allowing businesses to focus on delivering value through their AI systems.
Databricks Model Serving also offers unified management for all models, making it easy to access and manage custom ML models like PyFunc and scikit-learn, as well as foundation models hosted on Databricks or other providers. This unified user interface and API streamline the management process, enhancing user experience. Furthermore, the platform enables effortless batch inference, allowing for efficient, serverless AI inference on large datasets across various data types and models. Integration with Databricks SQL, Notebooks, and Workflows facilitates the application of AI at scale.
The platform is designed to be cost-effective, serving models as low-latency APIs on a highly available serverless service with both CPU and GPU support. Users can scale from zero to meet critical needs and back down as requirements change, with flexible pricing options available. Databricks handles infrastructure management and maintenance costs, enabling organizations to concentrate on delivering business value.
- CategoryMLOps & Model Deployment
- Pricing ModelPay-per-token
- DeploymentServerless
- PlatformsCloud
Mosaic AI Model Serving's Core Features
Unified deployment for AI models
Supports classical ML and generative AI models
Built-in governance and monitoring
Automated container build
Effortless batch inference
Serverless AI inference
Cost-effective low-latency API
Unified management interface
How to use Mosaic AI Model Serving?
Deploy: Choose your model type and deploy it on Databricks.
Integrate: Use served models as endpoints in your workflows.
Monitor: Utilize built-in governance and monitoring tools for quality assurance.
Scale: Adjust resources based on your workload needs.
Mosaic AI Model Serving's Use Cases
- Real-time recommendations
- Batch processing
- Model governance
- Custom model deployment
- Scalable AI applications
