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---
title: Fairness tab
description: Monitor the fairness of deployed production models over time.
---
# Fairness tab {: #fairness-tab }
!!! info "Availability information"
The **Fairness** tab is only available for DataRobot MLOps users. Contact your DataRobot representative for more information about enabling ... | mlops-fairness |
---
title: Notifications tab
description: Enable notifications, which trigger emails for service health and data drift reporting. Notifications are off by default but can be enabled by a deployment Owner. Configure Service Health, Data Drift, Accuracy, and Fairness monitoring.
---
# Notifications tab {: #notification... | deploy-notifications |
---
title: Governance
description: Model governance sets rules and controls for deployments, facilitates scaling of deployments, and provides legal and compliance reports.
---
# Governance {: #governance }
When machine learning models in production become critical to business functions, new requirements emerge to en... | index |
---
title: Model deployment approval workflow
description: DataRobot MLOps system administrators can specify security policies that control who can create or modify deployments and what kind of approval is required.
---
# Approval process {: #approval-process }
!!! info "Availability information"
The Model Depl... | dep-admin |
---
title: Humility tab
description: After configuring rules and making predictions with humility monitoring enabled, you can view the humility data collected over time for a deployment from the Humility tab.
---
# Humility tab {: #humility-tab }
After [configuring humility rules](humility-settings) and making predi... | humble |
---
title: Replace deployed models
description: How to replace deployment model packages, to keep models currrent and accurate. DataRobot uses training data to verify that the two models have the same target.
---
# Replace deployed models {: #replace-deployed-models }
Because model predictions tend to degrade in acc... | deploy-replace |
---
title: Lifecycle management
description: Lifecycle management provides tools and a robust, repeatable process to scale models and manage the lifecycle of models in production environments.
---
# Lifecycle management {: #lifecycle-management }
Machine learning models in production environments have a complex life... | index |
---
title: Manage deployments
description: Manage deployments using the actions menu, which allows you to apply deployment settings, share deployments, create applications using the deployed model, replace models, and delete deployments, among other actions.
---
# Manage deployments {: #manage-deployments }
On the D... | actions-menu |
---
title: Deployment settings
description: Add data to a deployment and configure monitoring, notifications, and challenger behavior using the Settings tab.
---
# Deployment settings {: #settings-tab }
!!! info "Deprecation notice"
The **Settings > Data** and **Settings > Monitoring** tabs are deprecated an... | deploy-settings |
---
title: Set up Automated Retraining policies
description: Maintain model performance after deployment through Automated Retraining.
---
# Set up Automated Retraining policies {: #set-up-automated-retraining-policies }
To maintain model performance after deployment without extensive manual work, DataRobot provides... | set-up-auto-retraining |
---
title: Deployment inventory
description: Learn about the deployment inventory, which displays all actively deployed models and lets you monitor deployed model performance and take necessary action.
---
# Deployment inventory {: #deployment-inventory }
Once models are deployed, the deployment inventory is the cen... | deploy-inventory |
---
title: Manage prediction environments
description: On the Prediction Environments page, you can edit, delete, or share external prediction environments. You can also deploy models to external prediction environments.
---
# Manage predictions environments
On the **Deployments > Prediction Environments** page, you... | pred-env-manage |
---
title: Add external prediction environments
description: You can manage and control user access to environments on the prediction environment dashboard and specify the prediction environment for any deployment.
---
# Add external prediction environments {: #add-external-prediction-environments }
Models that run... | pred-env |
---
title: Register external models
description: Register an external model package in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages.
---
# Register external models {: #register-external-models }
To create a model package for an external... | ext-model-reg |
---
title: Prepare external models
description: Prepare to create deployments from external models
---
# Prepare for external model deployment
External prediction environments and model packages allow you to deploy external (or remote) models to DataRobot. These models can make predictions on local infrastructure or... | index |
---
title: Manage external model packages
description: The Model Packages Actions menu allows users with appropriate permissions to share or permanently archive model packages.
---
# Manage external model packages
{% include 'includes/manage-model-packages.md' %} | ext-model-manage |
---
title: Prepare custom models
description: Prepare to create deployments from custom models
---
# Prepare custom models for deployment
Custom inference models allow you to bring your own pretrained models to DataRobot. By uploading a model artifact to the Custom Model Workshop, you can create, test, and deploy cu... | index |
---
title: Monitor an external model with the monitoring agent
description: How to monitor an external model with the monitoring agent.
---
# Monitor an external model with the MLOps agent {: #monitor-an-external-model-with-the-mlops-agent }
With DataRobot MLOps you can register an external model, create an external... | ext-cus-model-ext-env |
---
title: Scoring Code in an external environment
description: How to deploy an exported DataRobot model's Scoring Code in an external environment.
---
# Deploy Scoring Code in an external environment {: #deploy-scoring-code-in-an-external-environment }
With DataRobot MLOps you can register a DataRobot model, creat... | ext-dr-model-ext-env |
---
title: DataRobot model in a DataRobot environment
description: How to deploy a DataRobot model in a DataRobot environment.
---
# Deploy a DataRobot model in a DataRobot environment {: #deploy-a-datarobot-model-in-a-datarobot-environment }
DataRobot AutoML models allow you to deploy to a DataRobot-managed predict... | dr-model-dr-env |
---
title: Deployment workflows
description: An overview of the most common DataRobot deployment workflows for various model and environment type combinations.
---
# Deployment workflows
DataRobot's MLOps monitoring is available for any models deployed in DataRobot prediction environments (including models on your o... | index |
---
title: DataRobot model in a PPS
description: How to deploy a DataRobot model in a Portable Prediction Server.
---
# Deploy a DataRobot model in a Portable Prediction Server {: #deploy-a-datarobot-model-in-a-portable-prediction-server }
DataRobot AutoML models can be deployed to a containerized DataRobot predicti... | dr-model-pps-env |
---
title: Custom model in a DataRobot environment
description: How to deploy a custom model in a DataRobot prediction environment.
---
# Deploy a custom model in a DataRobot Environment {: #deploy-a-custom-model-in-a-datarobot-environment }
Custom inference models allow you to bring your pre-trained models into Dat... | cus-model-dr-env |
---
title: Custom model in a PPS
description: How to deploy a custom model in a Portable Prediction Server.
---
# Deploy a custom model in a Portable Prediction Server {: #deploy-a-custom-model-in-a-portable-prediction-server }
Custom inference models allow you to bring your pre-trained models into DataRobot through... | cus-model-pps-env |
---
title: Agent event log
description: On a deployment's Service Health tab, you can view Management and Monitoring events.
---
# Agent event log
On a deployment's **Service Health** tab, under **Recent Activity**, you can view **Management** events (e.g., deployment actions) and **Monitoring** events (e.g., spoole... | agent-event-log |
---
title: MLOps agents
description: Use the MLOPS agents to monitor and manage models running outside of DataRobot MLOps, and report predictions from these models as part of MLOps deployments. Learn about the MLOps agent workflows for DataRobot deployments and for remote deployments.
---
# MLOps agents {: #mlops-age... | index |
---
title: Register DataRobot models
description: Register a model package in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages.
---
# Register DataRobot models from the Leaderboard {: #register-datarobot-models-from-the-Leaderboard }
The Mod... | dr-model-reg |
---
title: Prepare DataRobot models
description: Prepare to create deployments from DataRobot models
---
# Prepare DataRobot models for deployment
DataRobot AutoML models allow you to deploy to a DataRobot-managed prediction environment. This deployment method is the most direct route to making predictions and monit... | index |
---
title: Manage DataRobot model packages
description: The Model Packages Actions menu allows users with appropriate permissions to share or permanently archive model packages.
---
# Manage DataRobot model packages
{% include 'includes/manage-model-packages.md' %} | dr-model-manage |
---
title: Model Registry
description: How model packages are created and added to the Model Registry, manually or automatically. The Model Registry is an archive of your model packages where you can deploy and share packages.
---
# Model Registry {: #model-registry }
The Model Registry is an organizational hub for ... | reg-create |
---
title: Generate model compliance documentation
description: Generate automated compliance documentation for models from the Model Registry.
---
# Generate model compliance documentation {: #generate-model-registry-compliance-documentation }
After you [create a model package](reg-create) in the Model Registry (th... | reg-compliance |
---
title: Manage model packages
description: The Model Registry Actions menu allows users with appropriate permissions to share or permanently archive model packages.
---
# Manage model packages {: #manage-model-packages }
{% include 'includes/manage-model-packages.md' %} | reg-action |
---
title: Import model packages into MLOps
description: Export a model created with DataRobot AutoML for import as a model package (.mlpkg file) in standalone MLOps environments.
section_name: MLOps
maturity: public-preview
platform: self-managed-only
---
# Import model packages into MLOps {: #import-model-packages-... | reg-transfer |
---
title: Register models
description: The Model Registry organizes all models used in DataRobot as deployment-ready model packages. All packages function the same way, regardless of model origin.
---
# Register models {: #register-models }
In the Model Registry, models are registered as deployment-ready model pack... | index |
---
title: Deploy external models
description: How to deploy external models by registering and deploying a model package or by uploading training data for the external model directly.
---
# Deploy external models {: #deploy-external-models }
You can deploy external (remote) models using either of the following meth... | deploy-external-model |
---
title: Deploy DataRobot models
description: How to create new deployments, deploy custom models, create deployments with training data, and add data post-deployment.
---
# Deploy DataRobot models {: #deploy-datarobot-models }
You can deploy models you build with DataRobot AutoML using the following methods:
* D... | deploy-model |
---
title: Configure deployment settings
description: When you add a deployment, configure the deployment by adding the prediction environment and enabling accuracy and data drift tracking, among other settings.
---
# Configure a deployment {: #configure-a-deployment }
Regardless of where you create a new deployment... | add-deploy-info |
---
title: Deploy custom inference models
description: How to deploy custom inference models, your pretrained models that you assemble in the Custom Model Workshop.
---
# Deploy custom inference models {: #deploy-custom-inference-models }
After you [create a custom inference model](custom-inf-model) using the Custo... | deploy-custom-inf-model |
---
title: Add prediction data post-deployment
description: How to add historical prediction data after a model is deployed.
---
# Add prediction data post-deployment {: #add-prediction-data-post-deployment }
Users with the [Owner](roles-permissions) role can add historical prediction data to deployments if data dri... | add-prediction-data-post-deploy |
---
title: Deploy models
description: How to create deployments from DataRobot models, custom inference models, and external models.
---
# Deploy models {: #deploy-models }
In DataRobot, the way you deploy a model to production depends on the type of model you start with and the prediction environment where the mode... | index |
---
title: Add training data to a custom model
description: How to assign training data to a custom model in the Custom Model Workshop.
---
# Add training data to a custom model {: #add-training-data-to-a-custom-model }
To enable feature drift tracking for a model deployment, you must add training data. To do this,... | custom-model-training-data |
---
title: Add custom model versions
description: Update a model's contents to create a new version of the model due to new package versions, different preprocessing steps, hyperparameters, etc.
---
# Add custom model versions {: #add-custom-model-versions }
If you want to update a model due to new package versions,... | custom-model-versions |
---
title: Manage custom model packages
description: The Model Packages Actions menu allows users with appropriate permissions to share or permanently archive model packages.
---
# Manage custom model packages
{% include 'includes/manage-model-packages.md' %} | custom-model-manage |
---
title: Register custom models as model packages
description: Register a custom model in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages.
---
# Register custom models as model packages {: #register-custom-models-as-model-packages }
You c... | custom-model-reg |
---
title: Manage custom models
description: How to use the Actions menu, which lets you share and delete custom models and environments.
---
# Manage custom models {: #manage-custom-models }
There are several **Actions** available from the menu on the **Model Registry** > **Custom Model Workshop** page, such as [sh... | custom-model-actions |
---
title: Manage custom model dependencies
description: Describes how to manage these dependencies from the Workshop and update the base drop-in environments to support your model code.
---
# Manage custom model dependencies {: #manage-custom-model-dependencies }
Custom models can contain various machine learning l... | custom-model-dependencies |
---
title: Custom Model Workshop
description: Using custom inference models, you can bring your own pretrained models into DataRobot. DataRobot supports models built with languages like Python, R, and Java.
---
# Custom Model Workshop {: #custom-model-workshop }
!!! info "Availability information"
The Custom Mod... | index |
---
title: Manage custom model resource usage
description: Configure the resources the model consumes to facilitate smooth deployment and minimize potential environment errors in production.
---
# Manage model resources {: #manage-model-resources }
After creating a custom inference model, you can configure the reso... | custom-model-resource-mgmt |
---
title: Create custom inference models
description: How to build a custom inference model in the Custom Model Workshop.
---
# Create custom inference models {: #create-custom-inference-models }
Custom inference models are user-created, pretrained models that you can upload to DataRobot (as a collection of files) ... | custom-inf-model |
---
title: Add files from remote repos to custom models
description: Add files from remote repositories, including Bitbucket, GitHub, GitHub Enterprise, S3, GitLab, and GitLab Enterprise to the models you create in the Custom Model Workshop.
---
# Add files from remote repos to custom models {: #add-files-from-remote... | custom-model-repos |
---
title: Test custom models
description: Follow the custom inference model testing workflow. Understand the types of tests employed and the insights available to verify performance, stability, and predictions.
---
# Test custom models {: #test-custom-models }
You can test custom models in the **Custom Model Worksh... | custom-model-test |
---
title: Drop-in environments
description: Describes DataRobot's built-in custom model environments.
---
# Drop-in environments {: #drop-in-environments }
DataRobot provides drop-in environments in the Custom Model Workshop. Drop-in environments contain the web server Scoring Code and a `start_server.sh` file req... | drop-in-environments |
---
title: Custom environments
description: Describes how to build a custom environment when a custom model requires something not contained in one of DataRobot's built-in environments.
---
# Custom environments {: #custom-environments }
Once uploaded into DataRobot, custom models run inside of environments—Do... | custom-environments |
---
title: Custom model environments
description: How to set up an environment for custom inference models created in the Custom Model Workshop.
---
# Custom model environments {: #custom-model-environments }
To [create a custom inference model](custom-inf-model), you must select an environment that the model will u... | index |
---
title: Custom model components
description: Describes custom model support and how to structure a custom model's files.
---
# Custom model components {: #custom-model-components }
To create and upload a custom model, you need to define two components—the model’s content and an environment where the model’s... | custom-model-components |
---
title: GitHub Actions for custom models
description: The custom models action manages custom inference models and deployments in DataRobot via GitHub CI/CD workflows.
---
# GitHub Actions for custom models {: #github-actions-for-custom-models }
The custom models action manages custom inference models and their a... | custom-model-github-action |
---
title: Assemble structured custom models
description: DataRobot provides built-in support for a variety of libraries to create models that use conventional target types.
---
# Assemble structured custom models
DataRobot provides built-in support for a variety of libraries to create models that use conventional t... | structured-custom-models |
---
title: DRUM CLI tool
description: DataRobot Model Runner (DRUM) is a tool that allows you to work with and test Python, R, and Java custom models and custom tasks.
---
{% include 'includes/drum-tool.md' %}
{% include 'includes/drum-for-ubuntu.md' %}
{% include 'includes/drum-for-mac.md' %}
{% include 'includes... | custom-model-drum |
---
title: Custom model assembly
description: Describes how to assemble custom models and environments.
---
# Custom model assembly {: #custom-model-assembly }
While DataRobot provides hundreds of built-in models, there are situations where you need preprocessing or modeling methods that are not currently supported ... | index |
---
title: Assemble unstructured custom models
description: Unstructured models can use arbitrary data for input and output, allowing you to deploy and monitor models regardless of the target type.
---
# Assemble unstructured custom models
If your custom model doesn't use a target type supported by DataRobot, you ca... | unstructured-custom-models |
---
title: Test custom models locally
description: Use the DataRobot Model Runner tool (DRUM) to test and verify a Python, R, or Java custom model locally, before you upload it to DataRobot.
---
# Test custom models locally {: #test-custom-models-locally }
!!! info "Availability information"
To access the DataR... | custom-local-test |
---
title: Relaunch deployments
description: Relaunch an MLOps management agent deployment without changes to the deployment's metadata,
---
# Relaunch management agent deployments
To manually relaunch a management agent deployment without changes to the deployment's metadata, you can trigger a relaunch from the dep... | mgmt-agent-relaunch |
---
title: Management agent deployment status and events
description: Monitor the status and health of MLOps management agent deployments.
---
# Management agent deployment status and events {: #management-agent-deployment-status-and-events }
To monitor the status and health of management agent deployments, you can ... | mgmt-agent-events-status |
---
title: Configure environment plugins
description: Configure prediction environment plugins for the MLOps management agent.
---
# Configure management agent environment plugins
Management agent plugins deploy and manage models in a given prediction environment. The management agent submits commands to the plugin,... | mgmt-agent-plugins |
---
title: Install the management agent for Kubernetes
description: Install and configure the MLOps management agent to use a Kubernetes Namespace as a Prediction Environment
---
# Management agent Helm installation for Kubernetes
This process provides an example of a management agent use case, using a Helm chart to... | mgmt-agent-kubernetes |
---
title: Management agent
description: Automate model deployment to any type of infrastructure and monitor deployment events.
---
# Management agent
The MLOps management agent provides a standard mechanism to automate model deployment to any type of infrastructure. It pairs automated deployment with automated moni... | index |
---
title: Force delete deployments
description: Delete a deployment without waiting for the resolution of the deployment deletion request sent to the management agent.
---
# Force delete management agent deployments
If the management agent is not running or has errored, you can delete a deployment without waiting f... | mgmt-agent-delete |
---
title: Installation and configuration
description: Install and configure the MLOps management agent.
---
# Management agent installation and configuration
The MLOps agent `.tar` file contains all artifacts required to run the management agent. You can run the management agent in either of the following configura... | mgmt-agent-install |
---
title: Installation and configuration
description: How to install and configure the monitoring agent to forward buffered messages from the MLOps library to DataRobot MLOps.
---
# Monitoring agent installation and configuration {: #monitoring-agent-installation-and-configuration }
When the monitoring agent is run... | agent |
---
title: Monitoring agent use cases
description: Investigate MLOPs reporting and monitoring use cases, including how to report metrics when the prediction environment isn't connected to DataRobot and how to monitor Spark environments.
---
# Monitoring agent use cases {: #monitoring-agent-use-cases }
Reference the u... | agent-use |
---
title: Environment variables
description: Describes the environment variables specific to operating the monitoring agent.
---
# Monitoring agent environment variables {: #monitoring-agent-environment-variables }
In addition to the environment variables used to configure the attached [spooler](spooler), you can c... | env-var |
---
title: Examples directory
description: Use sample code available in the MLOps agent tarball as a starting point for creating and managing deployments. Examples include model configuration, data, and scripts used to create deployments and run the examples.
---
# Examples directory {: #examples-directory }
The `ex... | agent-ex |
---
title: Monitoring agent
description: Set up a remote environment so you can use the monitoring agent to monitor external models.
---
# Monitoring agent
When you enable the monitoring agent feature, you have access to the agent installation and MLOps components, all packaged within a single tarball. The image bel... | index |
---
title: Download Scoring Code
description: How to download a deployment’s monitoring agent with Scoring Code.
---
# Download Scoring Code {: #download-scoring-code }
You can download the monitoring agent packaged with [Scoring Code](scoring-code/index) directly from a deployment.
!!! note
Note that the deplo... | agent-sc |
---
title: Library and agent spooler configuration
description: How to configure the MLOps library and monitoring agent spooler so that the library can communicate with the agent through the spooler.
---
# MLOps library and agent spooler configuration {: #mlops-library-and-agent-spooler-configuration }
The MLOps lib... | spooler |
---
title: Monitoring external multiclass deployments
description: How to configure the monitoring agent to monitor multiclass models deployed to external prediction environments.
---
# Monitor external multiclass deployments {: #monitor-external-multiclass-deployments }
Users with multiclass models deployed to exte... | agent-multi |
---
title: Algorithmia
description: Algorithmia is an MLOps platform where you can deploy, govern, and monitor your models as microservices.
---
# Algorithmia
DataRobot Algorithmia is an MLOps platform where you can deploy, govern, and monitor your models as microservices. The platform lets you connect models to data... | algorithmia |
---
title: Companion tools
description: This section includes links to user documentation for DataRobot companion tools, including Algorithmia and Data Prep (Paxata).
---
# Companion tools {: #companion-tools }
The information in this section provides user documentation for Data Prep and Notebooks.
Topic | Describes... | index |
---
title: MLOps compatibility
description: DataRobot's MLOps feature availability is based on version. This page explains which features are accessible to each plan.
---
# MLOps compatibility {: #mlops-compatibility }
DataRobot offers various plans to users, each with its own set of features. Reference this page to... | pricing |
---
title: Value Tracker
description: Helps you to measure success with using DataRobot by defining the value you expect to get and tracking the actual value you receive in real time.
---
## Value Tracker {: #value-tracker }
The Value Tracker allows you to specify what you expect to accomplish by using DataRobot. Yo... | value-tracker |
---
title: Account and project management
description: This section introduces the management toolbar and includes links to information on how you can manage account settings.
---
# Account and project management {: #account-and-project-management }
The information in this section provides information to help manage ... | index |
---
title: Notification center
---
# Notification center {: #notification-center }
The alert icon () provides access to notifications sent from the DataRobot platform. A numeric indicator on top of the alert icon indicates that you have unread notifications.
Clic... | user-notif-center |
---
title: Project control center
description: Use the project control center to quickly access recently used projects and the Manage Projects inventory.
---
# Manage projects {: #manage-projects }
Each DataRobot project includes a dataset, which is the source used for training, and any models built from that dataset... | manage-projects |
---
title: Help resources
---
# Help resources {: #help-resources }
Click the question mark icon in the upper right navigation.
From this menu you can access:
Option | Description
---------- | -----------
**Documentation** | :~~:
UI Documentation | Documentation for UI-based DataR... | getting-help |
---
title: Workflow overview
description: Overview of typical admin workflow for creating user accounts, defining groups, assigning access roles, monitoring and managing worker allocation, and more.
---
# DataRobot overview and administrator workflow {: #datarobot-overview-and-administrator-workflow }
DataRobot sets... | admin-overview |
---
title: Manage feature settings
description: With the proper access and permissions, you can view and manage feature settings and permissions for your account and for other users.
---
# Manage feature settings {: #manage-feature-settings }
With the proper access and permissions, you can view and manage feature se... | user-settings |
---
title: Manage groups
description: Learn about creating and deleting groups, adding users to groups, setting group permissions, and role-based access control (RBAC) for groups.
---
# Manage groups {: #manage-groups }
=== "SaaS"
!!! info "Availability information"
**Required permission:** Org Admin
=== "... | manage-groups |
---
title: Manage organizations
description: How to create an organization if you wish to restrict user access to workers. You can prevent members from sharing the organization's projects to outside users or groups.
---
# Manage organizations {: #manage-organizations }
!!! info "Required permission"
“Can manage u... | manage-orgs |
---
title: Administrator's guide
description: Help for system administrators in managing DataRobot Self-Managed AI Platform deployments.
---
# Administrator's guide {: #administrators-guide }
=== "SaaS"
The _DataRobot Administrator's Guide_ is intended to help administrators manage their DataRobot application. Bef... | index |
---
title: Manage user accounts
description: How to create LDAP or local authentication user accounts, set permissions, and manage membership of groups and organizations.
---
# Manage user accounts {: #manage-user-accounts }
The DataRobot deployment provides support for local authentication users. These are user acc... | manage-users |
---
title: Deployment approval policies
description: To enable effective governance and control across DataRobot, admins can create and modify global approval policies for deployment-related activities.
---
# Deployment approval policies {: #deployment-approval-policies }
!!! info "Availability information"
**R... | deploy-approval |
---
title: User Activity Monitor
description: The User Activity Monitor (UAM) provides a means for accessing and analyzing various usage data and prediction statistics as online reports or via export.
---
# User Activity Monitor {: #user-activity-monitor }
!!! info "Availability information"
For Managed Cloud A... | main-uam-overview |
---
title: Roles and permissions
description: Describes the many layers of security DataRobot employs to help protect customer data through controlled user-assigned access levels.
---
# Roles and permissions {: #roles-and-permissions }
DataRobot employs many layers of security to help protect customer data—at t... | roles-permissions |
---
title: Data and sharing
description: This section introduces sharing within DataRobot, including how roles and permissions affect sharing.
---
# Sharing {: #sharing }
The information in this section provides information on data and sharing requirements throughout DataRobot.
Topic | Describes...
----- | ---------... | index |
---
title: Sharing
description: How to share DataRobot datasets, projects, and deployments.
---
# Sharing {: #sharing }
You can share datasets, projects, and deployments ("assets") within your organization. You may want to do this, for example, to get the assistance of an in-house data scientist who has offered to h... | sharing |
---
title: Personal data detection
description: DataRobot automates the detection of specific types of personal data to provide a layer of protection against the inadvertent inclusion of this information modeling and predictions.
section_name: Administrator
maturity: public-preview
platform: self-managed-only
---
# P... | pii-detect |
---
title: Administrator public preview features
description: Read preliminary documentation for administration features currently in the DataRobot public preview pipeline.
section_name: Administrator
maturity: public-preview
platform: self-managed-only
---
# Administrator public preview features {: #administrator-pub... | index |
---
title: Two-factor authentication (2FA)
description: How to set up two-factor authentication (2FA), an opt-in feature that provides additional security for DataRobot users.
---
# Two-factor authentication {: #two-factor-authentication }
Two-factor authentication (2FA) is an opt-in feature that provides additional ... | 2fa |
---
title: SAML SSO
description: How to configure DataRobot and an external Identity Provider (IdP) for user authentication via single sign-on (SSO). DataRobot supports the SAML 2.0 protocol.
---
# SAML SSO {: #saml-sso }
DataRobot allows you to use external services (Identity Providers, known as IdPs) for user auth... | sso-ref |
---
title: Authentication
description: This section introduces authentication in DataRobot and includes links to information on SSO, 2FA, stored data credentials, and API key management.
---
# Authentication {: #authentication }
The information in this section provides information on authentication in DataRobot.
Top... | index |
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