# First time here?
URL: https://docs.datarobot.com/en/docs/get-started/day0/index.html

> Get started with DataRobot's AI Platform. After a quick orientation, jump into suggested learning exercises or browse sample assets and tutorials.

# First time here?

> [!NOTE] Note
> Prefer to learn with video? Visit the [video learning page](https://docs.datarobot.com/en/docs/get-started/day0/video-learning.html) for a list of videos with brief descriptions. Or, go straight to YouTube to view the [onboarding series](https://www.youtube.com/watch?v=LaQCLRX64qE&list=PLe-6XGmzriIjU4DyqNpKYZ6Eb_liRDhaV) to help get started with DataRobot.

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 NIM 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 inWorkbench. What is Workbench?Workbenchis 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 is a blueprint?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 thegraphical representationof a blueprint by clicking on a model on the Leaderboard.
2. Register and manage deployment-ready model packages inRegistry. What is Registry?Registryis 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 theConsolemanagement dashboard. What is Console?Consoleis 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.

| Topic | Description |
| --- | --- |
| DataRobot AI Platform overview | Get a quick overview of the fundamentals of the DataRobot AI Platform: what it is, why you need it, and which user path to get started on. |
| Foundational apps | Learn the basics of Foundational apps—DataRobot's solution to the challenge of scaling AI from local experiments to production systems. |
| Start with Agent Assist | Learn about Agent Assist (dr-assist), an interactive AI assistant that helps users design, code, and deploy AI agents through natural conversation. |
| Start with GenAI | Learn the basics of building a GenAI workflow—an overview of fundamental concepts and sample workflow. |
| Start with predictive modeling | Understand the types of modeling projects you can create in DataRobot and review a generalized discussion of the steps to build predictive models. |
| Suggested first steps | Try a generative or predictive walkthrough to get started. |
| Sample assets | View sample assets by type with links to corresponding tutorials. |
| Video learning | See a list of onboarding videos, with brief descriptions, to help get started with DataRobot. |
| Trial FAQ | Questions and answers about the DataRobot Self-Service SaaS trial. |

---
