First time here?¶

The DataRobot Agentic AI Platform seamlessly orchestrates the full lifecycle of AI agents across a wide range of deployment environments, including on-prem and private GPU clouds, sovereign GPU clouds, and major public cloud platforms. Optimized for the next-generation GPU architecture and tightly integrated with NVIDIA pre-built blueprints, DataRobot enables faster, more secure deployment of AI solutions that drive real business impact.

The DataRobot interface provides a seamless transition from model experimentation in Workbench, to registering and managing models in Registry, to monitoring model deployments in Console.

Key Highlights:

• End-to-End orchestration and lifecycle management: Develop, deliver, and govern AI agents seamlessly.

• Deployment flexibility: Available across cloud partners, sovereign GPU clouds, on-prem/private GPU clouds, and major public clouds.

• NVIDIA-powered performance: Optimized for Blackwell GPUs, integrated with NIMs and NeMo, with blueprints for rapid deployment.

• Enterprise-grade governance: Ensures secure, compliant, and scalable AI deployments.

• Validated design: Delivers streamlined, production-ready AI.

Develop, deliver, govern¶

1. Build LLM or predictive blueprints and tune models in Workbench.

   What is Workbench?

   Workbench is an experiment-based user interface optimized to support iterative workflow. It enables users to group and share everything they need to solve a specific problem from a single location. Workbench is organized by Use Case, and each Use Case contains zero or more datasets, vector databases, playgrounds, models, notebooks, and applications.

   What are blueprints?

   A blueprint is a graphical representation of the many steps involved in transforming input predictors and targets into a model. It represents the high-level end-to-end procedure for fitting the model, including any preprocessing steps, algorithms, and post-processing. Each box in a blueprint may represent multiple steps. You can view the graphical representation of a blueprint by clicking on a model on the Leaderboard.

2. Register and manage deployment-ready model packages in Registry.

   What is Registry?

   Registry is a centralized location where you access versioned, deployment-ready model packages. From there, you can create custom models and jobs, generate compliance documentation, and deploy models to production.

3. Monitor deployment activity from the Console management dashboard.

   What is Console?

   Console is a central hub for deployment management activity. Its dashboard provides access to deployed models for further monitoring and mitigation. It also provides access to prediction activities and allows you to view, create, edit, delete, or share serverless and external prediction environments

To get started quickly in the DataRobot AI Platform, take a look at the overview and then jump directly into the two suggested learning exercises. Afterwards, continue your learning with more sample assets and tutorials.