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Version: Cloud

Tutorials

In this section, you can find quick and easy video tutorials to get you started on using Deeploy.

Organize your Git repository for models & explainers​

In this step-by-step tutorial, you'll learn to prepare your Git repository so you can deploy AI models and explainers in Deeploy.

UI​

The UI tutorials guide you step by step on how to work with Deeploy through its interface.

Deploy your first model & explainer​

Learn how to get your first model and explainer up and running in Deeploy.

Explore organizations​

Explore the functionalities for admins of an organization in Deeploy: The model registry overview, logs, configuration of integrations, user management, and billing.

Explore Workspaces​

Explore the functionalities of a Workspace: Overview of deployments, repositories, use cases, compliance templates, credentials, Workspace member management, environment variables, and job schedules.

Explore use cases​

Explore use cases, which provide a higher-level view of your Deployments. Each use case can include multiple Deployments, tracked across their lifecycle stages and associated risk classifications. In line with the EU AI Act, these risk levels are determined through the risk classification assessment.

Manage your Deployments​

Explore the functionalities of a Deployment: Dashboard, monitoring, alerts, compliance documentation, details, version history, prediction history, events, container logs, code snippets, authentication and testing features.

Onboard your external models​

In this tutorial, we show you how to connect a model deployed on another platform, like an OpenAI GPT-4o model on Azure, to Deeploy as an external Deployment. You’ll learn how to bring your externally hosted models into Deeploy to use its governance and monitoring features with minimal setup.

Using control frameworks​

Explore control frameworks that translate regulatory requirements and internal AI policies into actionable controls. Each framework runs automated checks across your use cases and Deployments to maintain continuous compliance and audit readiness.

Explore documentation​

Explore the types of documentation you can add to Deeploy at both the organization and Deployment levels.

Setting up guardrails​

Learn how to configure guardrails in Deeploy to protect model inputs and outputs from prompt injection, data leakage, and accidental disclosure of personal information. Guardrails let you detect risky patterns (regexes, keyword lists, or policy rules). Use them to enforce privacy and safety consistently across deployments while keeping a record for compliance and debugging.

Python client​

The Python client tutorials guide you step by step on how to work with Deeploy through the Deeploy Python client.

Deploy models & explainers with the Deeploy Python client​

In this tutorial, you’ll learn how to deploy a model and explainer from a prepared Git repository to Deeploy using the Deeploy Python Client.

Make predictions & explanations with the Deeploy Python client​

In this tutorial, you’ll learn how to use the Deeploy Python Client to generate predictions and explanations from your deployment in Deeploy.

We’ll walk you through:

  • Sending prediction requests to your deployed model
  • Retrieving model explanations for transparency
  • Using the client to streamline testing and validation

Submit actuals & evaluations with the Deeploy Python client​

In this tutorial, you’ll learn how to use the Deeploy Python client to submit actuals and evaluations to your deployment in Deeploy.

We’ll cover how to:

  • Send actual outcomes to your deployment
  • Log evaluations for performance tracking
  • Close the loop between model predictions and real-world results