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▶︎Cloudera
1. Reference Architectures
▼Cloudera on cloud
▶︎Cloudera on premises
▶︎Cloudera Manager
1. Cloudera Manager
▶︎Applications
▶︎Legacy
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Getting Started
Patterns
Preview Features
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Data Engineering
DataFlow
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Data Warehouse
Data Warehouse Runtime
Cloudera AI
Management Console
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Replication Manager
DataFlow for Data Hub
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Cloudera AI
▶︎Top Tasks
1. ▶︎Top Tasks for Cloudera AI
▶︎Release Notes
▶︎ML Runtimes Release Notes
1. ▶︎ML Runtimes What's New
2. ▶︎ML Runtimes Known Issues and Limitations
▶︎ML Runtimes Pre-installed Packages
▶︎Product Overview
1. Cloudera AI Overview
▶︎Planning
▼How To
▶︎Troubleshooting
▶︎Reference

(Optional) Configure the Sync between Cloudera AI and PyCharm
(Optional) Using VS Code with Git integration
(Optional) Using VS Code with Jupyter
(Optional) Using VS Code with Python
(Optional) Using VS Code with R
Accelerators for ML Projects (AMP)
Access Keys for Models
Access the Terminal
Accessing Cloudera AI Inference service Metrics
Accessing Cloudera AI Workbenches via SOCKS Proxy
Accessing Data from HDFS
Accessing data with Spark
Accessing Environmental Variables from Projects
Accessing environmental variables from projects
Accessing local files in Ozone
Accessing Ozone storage
Add a catalog
Add Cloudera AI as an Interpreter for PyCharm
Add Docker registry credentials
Adding a Collaborator
Adding a new customized ML Runtime through the Runtime Catalog
Adding a Node Group to an existing instance
Adding a node group to an existing instance using the UI
Adding an SSH key to GitHub
Adding Custom ML Runtimes through the Runtime Catalog
Adding Docker registry credentials
Adding Hadoop CLI to ML Runtime Sessions
Adding ML Runtimes using Runtime Repo files
Adding new ML Runtimes
Adding Project Collaborators
Adding Spark to ML Runtime Sessions
AMP Project Specification
AMPs in airgapped environment with Proxy
AMPs in airgapped environments
AMPs in fully airgapped environments
Analytical Applications
Analytical Applications
Apache Spark supported versions
API
API Key for Models
API Permissions For Projects
API v2 usage
Application polling endpoint
Applications
April 14, 2020
April 21, 2022
April 25, 2024
April 26, 2023
April 5, 2023
Architecture Overview
Architecture Overview
August 04, 2020
August 22, 2019
August 23, 2021
August 30, 2022
August 31, 2021
August 31, 2023
Authenticating clients for interacting with Cloudera AI Registry API
Authentication of Cloudera AI Inference service
Authorization of Cloudera AI Inference service
Automatic Logging
Autoscale Groups
Autoscaling Model Endpoints using API
Autoscaling Workloads with Kubernetes
AWS Account Prerequisites
AWS Cloud
AWS IAM restricted roles and policies for compute and Cloudera AI
Azure Account Requirements
Azure Cloud
Azure Environment Setup
Azure NetApp files management with the CLI
Backing up an Cloudera AI Workbench
Backing up Cloudera AI Workbenches
Backing up workbench
Base Engine 10-cml1.3
Base Engine 11-cml1.4
Base Engine 12-cml-2020.06-2
Base Engine 13-cml-2020.08-1
Base Engine 14-cml-2021.05-1
Base Engine 15-cml-2021.09-1
Base Engine 9-cml1.2
Build and deployment commands for models
Build the New Docker Image
Build the new Docker image
Built-in Cloudera AI Visualizations
Burstable CPUs
Business Users and Cloudera AI
Business Users and Cloudera AI
Catalog File Specification
cdswctl Command Line Interface Client
cdswctl command reference
Certified scale limitations for Cloudera AI Workbenches on AWS
Challenges with Machine Learning in production
Challenges with model deployment and serving
Challenges with model governance
Challenges with model monitoring
Changing Docker credential setting for ML Runtime
Choosing default engine
CLI
Cloudera Accelerators for Machine Learning Projects
Cloudera AI
Cloudera AI and outbound network access
Cloudera AI API v2
Cloudera AI Architecture
Cloudera AI email notifications
Cloudera AI Experiment Tracking through MLflow API
Cloudera AI Inference service
Cloudera AI Inference service cannot discover Cloudera AI Registry
Cloudera AI Inference service Concepts
Cloudera AI Inference service Configuration and Sizing
Cloudera AI Inference service using OpenAI Python SDK client in a Cloudera AI Workbench Session
Cloudera AI Inference service using OpenAI Python SDK client on a local machine
Cloudera AI Overview
Cloudera AI Project Lifecycle
Cloudera AI Registry CLI Client
Cloudera AI Registry permissions
Cloudera AI Registry standalone API
Cloudera AI service with Data Lake upgrades
Cloudera AI upgrades using Backup/Restore
Cloudera AI Workbenches
Cloudera Data Visualization for Cloudera AI
Collaboration Models
Command Line Tools in Cloudera AI
Compatibility for Cloudera AI and Runtime components
Configuration options
Configure a browser-based IDE as an Editor
Configure PyCharm as a local IDE
Configure PyCharm to use Cloudera AI as the remote console
Configure VS Code as a local IDE
Configuring a browser-based IDE at the Project level
Configuring a local IDE using an SSH gateway
Configuring Cloudera Copilot
Configuring external authentication with LDAP and SAML
Configuring HTTP Headers for Cloudera AI
Configuring LDAP/Active Directory authentication
Configuring model request payload size
Configuring Project-level Runtimes
Configuring Quotas
Configuring SAML authentication
Configuring the engine environment
Configuring Traefik readTimeout for large file uploads
Configuring User access to Cloudera AI
Connect to a Cloudera Data Hub cluster
Connect to Cloudera Data Lake
Connect to External SQL Databases
Connecting to Cloudera Data Warehouse
Connecting to external Amazon S3 buckets
Connecting to Ozone filesystem
Controlling User access to features
Create a Dockerfile for the custom image
Create a Dockerfile for the Custom Runtime Image
Create a model using the CLI
Create an Iceberg data connection
Create Azure Files Storage Account and File Share
Create Azure NetApp Files Account, Capacity Pool and Volume
Create IAM roles and instance profile pair
Create role and policy used to deploy Cloudera environments for Cloudera AI
Create subnets
Creating a Cloudera AI Inference service instance
Creating a Cloudera AI Inference service instance using the UI
Creating a Cloudera AI Registry
Creating a Cloudera AI Registry on an Azure UDR Private Cluster
Creating a customized engine image
Creating a Job
Creating a machine user and synchronizing to workbench
Creating a model
Creating a Model Endpoint using API
Creating a Model Endpoint using UI
Creating a model using MLflow
Creating a new version of a registered model
Creating a Pipeline
Creating a project from a password-protected Git repo
Creating a Project with Legacy Engine Variants
Creating a Project with ML Runtimes variants
Creating a Team
Creating a Team
Creating an SSH tunnel
Creating and deploying a Model
Creating Customized ML Runtimes
Creating New AMPs
Creating New AMPs using API
Creating Resource profiles
Creating Resource profiles
Critical and Non-critical Pods
Custom AMP Catalog
Custom Data Connection Development
Custom Runtime Addons with Cloudera AI
Custom Template Projects
Customized engine images
Customized Runtimes
CVE-2021-44228 Remediation for Cloudera AI Data Service
Data Access
Data Access
Data connection management
Debugging common Cloudera AI errors
Debugging issues with Models
Debugging the model import failure
December 13, 2021
December 15, 2021
December 15, 2023
December 19, 2019
December 21, 2020
Delete a model from Cloudera AI Registry
Deleting a Model
Deleting a Model Endpoint using API
Deleting a node group from an existing Cloudera AI Inference service instance
Deleting a node group from an existing Cloudera AI Inference service instance using the UI
Deleting a Project
Deleting a registered model version
Deleting Cloudera AI Inference service instance
Deleting Cloudera AI Inference service instances using the UI
Deleting Cloudera AI Registry
Deploy a new model with updated resources
Deploying a model from Registered Models
Deploying a model from the AI Registry page
Deploying a model from the Cloudera AI Registry using APIv2
Deploying a model from the destination Project page
Deploying an MLflow model as a Cloudera AI Model REST API
Deploying models with Canary deployment using API
Deploying Predictive Models
Deploying the Model
Describing a Model Endpoint using API
Describing Cloudera AI Inference service instance
Developing and testing your first custom connection
Disable or deprecate Runtime addons
Disabling and Deleting Runtimes
Disabling Cloudera AI Registry
Distribute the Image
Distribute the image
Distributed Computing
Distributed Computing with Workers
Dockerfile compatible with PBJ Workbench
Documenting Your Analysis
Download and configure cdswctl
Download cdswctl and add an SSH Key
Downloading diagnostic bundles for a workbench
Downloading diagnostic bundles for a workbench
Editing a Model Endpoint Configuration using UI
Editing model visibility
Editor Customization
Embed a Cloudera AI application in an external website
Embedded Web Applications
Enable a private cluster
Enable Cross-Origin Resource Sharing (CORS)
Enable HTTP security headers
Enable HTTP Strict Transport Security (HSTS)
Enabling authentication
Enabling model governance
Enabling model metrics
End-to-end example: MeCab
Engine environment variables
Engine environment variables
Environmental Variables
Ephemeral storage
Evaluating LLM with MLFlow
Example - Model training and deployment (Iris)
Example models with PBJ Runtimes
Example: A Shiny Application
Example: Connect a Spark session to the Data Lake
Example: Flask application
Example: Locating and adding JARs to Spark 2 configuration
Example: Monte Carlo estimation
Experiments
Experiments with MLflow
Exploratory Data Science and Visualization
Exploratory Data Science and Visualization
Exploratory Data Science and Visualization
Export Usage List
February 07, 2025
February 10, 2022
February 10, 2023
February 13, 2020
February 14, 2023
February 20, 2024
February 3, 2021
February 7, 2023
February 8, 2024
Force delete a Cloudera AI Registry
Generating an API key
Git for Collaboration
GPUs
Grant remote access to Cloudera AI Workbench
Granting Cloudera Users access to Cloudera AI Workbenches
Grid Displays
Hadoop authentication for Cloudera AI Workbenches
Handling project volume size increase in Cloudera AI
Host name required by Learning Hub
HTML Visualizations
HuggingFace Spaces and Community AMPs
IFrame Visualizations
Importing a Hugging Face Model (Technical Preview)
Importing Models
Importing models from Hugging Face (Technical Preview)
Importing models from NVIDIA NGC
Including images in allowlist for Cloudera AI projects
Initialize an SSH connection to Cloudera AI for VS code
Initialize an SSH Endpoint
Installing a Jupyter extension
Installing a Jupyter kernel
Installing a non-transparent proxy in a Cloudera AI environment
Installing Additional ML Runtimes Packages
Installing additional packages
Instance type preflight check fails
Interacting with Model Endpoints
January 12, 2022
January 23, 2024
January 29, 2025
January 30, 2020
Jobs and Pipelines
Jobs API
July 12, 2023
July 17, 2024
July 21, 2022
July 25, 2023
July 8, 2021
June 11, 2024
June 20, 2024
June 24, 2021
June 30, 2020
June 9, 2020
Jupyter Magic Commands
Jupyter Magics
Key Applications
Key Features
Known issues
Known Issues and Limitations
Known issues and limitations
Known Issues and Limitations in ML Runtimes older releases
Known Issues and Limitations in ML Runtimes version 2024.05.02
Known Issues and Limitations in ML Runtimes version 2024.10.01
Known Issues and Limitations in ML Runtimes version 2025.01.01
Known Issues and Limitations in ML Runtimes version 2025.01.2
Known Issues and Limitations in ML Runtimes version 2025.01.3
Known Issues and Limitations with Model Builds and Deployed Models
Known issues with Cloudera AI Registry standalone API
Launch a Session
LDAP general settings
LDAP group settings
Legacy Engine level configuration
Legacy Engines
Limitations
Limitations and Restrictions
Limitations on AWS
Limitations on Azure
Limitations with Analytical Applications
Limitations with customized engines
Limiting files in Explorer view
Linking an existing Project to a Git remote
Listing Cloudera AI Inference service instances
Listing Cloudera AI Inference service instances using the UI
Listing Model Endpoints using API
Listing Model Endpoints using UI
Listing runtimes
Loading custom connections
Log into cdswctl
Logs for Cloudera AI Workbenches
Making an inference call to a Model Endpoint with an OpenAI API
Making an inference call to a Model Endpoint with Open Inference Protocol
Managing a Synced Team
Managing a Team Account
Managing a Team Account
Managing API Keys
Managing Cloudera AI Inference service
Managing Cloudera AI Inference service using Cloudera CLI
Managing Cloudera AI Inference service using the UI
Managing Cloudera Copilot
Managing default and backup data connections
Managing dependencies for Spark 2 and Scala
Managing dependencies for Spark 2 jobs
Managing Engines
Managing memory available for Spark drivers
Managing ML Runtimes
Managing Model Endpoints using API
Managing Model Endpoints using UI
Managing Node Groups
Managing node groups using the UI
Managing Project Files
Managing Projects
Managing Users
Managing your Personal Account
Managing your Personal Account
March 14, 2022
March 16, 2020
March 22, 2021
March 27, 2023
March 6, 2024
May 13, 2021
May 15, 2024
May 16, 2023
May 29, 2024
May 31, 2022
May 31, 2023
May 5, 2020
Metadata for Custom ML Runtimes
Migrating from generic NFS to Azure Files NFS in Cloudera AI
ML Runtimes
ML Runtimes 2020.11
ML Runtimes 2021.02
ML Runtimes 2021.04
ML Runtimes 2021.06
ML Runtimes 2021.09
ML Runtimes 2021.12
ML Runtimes 2022.04
ML Runtimes 2022.11
ML Runtimes 2023.05
ML Runtimes 2023.08
ML Runtimes 2023.08.2
ML Runtimes 2023.12.1
ML Runtimes 2024.02.1
ML Runtimes 2024.05.1
ML Runtimes 2024.05.2
ML Runtimes 2024.10.1
ML Runtimes 2025.01.1
ML Runtimes 2025.01.2
ML Runtimes 2025.01.3
ML Runtimes Environment Variables
ML Runtimes Environment Variables List
ML Runtimes Known Issues and Limitations
ML Runtimes NVIDIA GPU Edition
ML Runtimes NVIDIA RAPIDS Edition
ML Runtimes Pre-installed Packages
ML Runtimes Pre-installed Packages overview
ML Runtimes Release Notes
ML Runtimes Version 2020.11
ML Runtimes Version 2021.02
ML Runtimes Version 2021.04
ML Runtimes Version 2021.06
ML Runtimes Version 2021.09
ML Runtimes Version 2021.09.02
ML Runtimes Version 2021.12
ML Runtimes Version 2022.04
ML Runtimes Version 2022.11
ML Runtimes Version 2022.11.2
ML Runtimes Version 2023.05.1
ML Runtimes Version 2023.05.2
ML Runtimes Version 2023.08.1
ML Runtimes Version 2023.08.2
ML Runtimes Version 2023.12.1
ML Runtimes versus Legacy Engine
ML Runtimes What's New
MLflow transformers
Model access control
Model explainability, interpretability, and reproducibility
Model Governance
Model governance using Apache Atlas
Model Hub
Model Hub in air-gapped installations
Model Metrics
Model visibility
Models
Models
Models - Concepts and Terminology
Modes of configuration
Modify Instance Group Type
Modifying a node group in an existing instance
Modifying a node group in an existing instance using the UI
Modifying Project Settings
Monitoring Active Models
Monitoring active Models
Monitoring and alerts
Monitoring applications
Monitoring Cloudera AI Workbenches
Monitoring User Activity
Monitoring User Events
Native Workbench Console and Editor
Network Planning for Cloudera AI on AWS
Network Planning for Cloudera AI on Azure
Non-transparent proxy and egress trusted list
November 1, 2019
November 15, 2023
November 21, 2024
November 23, 2020
November 29, 2022
Obtaining Control Plane Audit Logs for Cloudera AI Inference service
Obtaining Control Plane Audit Logs for Cloudera AI Inference service using the UI
Obtaining kubeconfig on AWS
Obtaining kubeconfig on Azure
Obtaining the kubeconfig for the Cloudera AI Inference service Cluster
Obtaining the kubeconfig of Cloudera AI Inference service using the UI
October 10, 2024
October 18, 2021
October 19, 2022
October 19, 2023
October 27, 2021
October 29, 2020
October 8, 2024
Older releases
Onboarding Business Users
Open Inference Protocol Using Curl
Open Inference Protocol Using Python SDK
OpenAI Inference Protocol Using Curl
Other NFS Options
PBJ R 3.6 Libraries
PBJ R 4.0 Libraries
PBJ R 4.1 Libraries
PBJ Runtimes and Models
PBJ Workbench
Personal key
Ports used by Cloudera AI
Pre-Installed Packages in engines
Pre-Installed Packages in ML Runtimes
Preflight checks
Prepare to manage models using the model CLI
Preparing to interact with the Cloudera AI Inference service API
Prerequisites for Cloudera AI discovery and exploration
Prerequisites for Cloudera AI Registry standalone API
Prerequisites for setting up Cloudera AI Inference service
Prerequisites to set up Cloudera Copilot
Private cluster support
Product Overview
Project garbage collection
Projects
Provisioning
Provisioning Cloudera AI Workbenches
Python
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.12 Libraries for Conda
Python 3.12 Libraries for Conda
Python 3.12 Libraries for Conda
Python 3.12 Libraries for JupyterLab
Python 3.12 Libraries for JupyterLab
Python 3.12 Libraries for JupyterLab
Python 3.12 Libraries for JupyterLab
Python 3.12 Libraries for Workbench
Python 3.12 Libraries for Workbench
Python 3.12 Libraries for Workbench
Python 3.12 Libraries for Workbench
Python 3.6 Libraries for JupyterLab
Python 3.6 Libraries for JupyterLab
Python 3.6 Libraries for Workbench
Python 3.6 Libraries for Workbench
Python 3.6.12 Libraries for JupyterLab
Python 3.6.12 Libraries for Workbench
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for PBJ Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7.9 Libraries for JupyterLab
Python 3.7.9 Libraries for Workbench
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for PBJ Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8.6 Libraries for JupyterLab
Python 3.8.6 Libraries for Workbench
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for PBJ Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Querying the engine type
R 3.6 Libraries
R 3.6 Libraries
R 3.6 Libraries
R 3.6 Libraries
R 3.6 Libraries
R 3.6 Libraries
R 3.6 Libraries
R 3.6 Libraries
R 3.6 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.1 Libraries
R 4.1 Libraries
R 4.1 Libraries
R 4.1 Libraries
R 4.1 Libraries
R 4.1 Libraries
R 4.3 Libraries
R 4.3 Libraries
R 4.3 Libraries
R 4.3 Libraries
R 4.4 Libraries
R 4.4 Libraries
R 4.4 Libraries
R 4.4 Libraries
R 4.4 Libraries
R 4.4 Libraries
RAPIDS Runtime PIP Python 3.7.8 Libraries for JupyterLab
RAPIDS Runtime PIP Python 3.7.8 Libraries for Workbench
RAPIDS Runtime PIP Python 3.8.6 Libraries for JupyterLab
RAPIDS Runtime PIP Python 3.8.6 Libraries for Workbench
Recommended troubleshooting workflow
Register an ONNX model to Cloudera AI Registry
Registered Models
Registering a model using MLflow SDK
Registering a model using the AI Registry user interface
Registering and deploying models with Cloudera AI Registry
Registering training data lineage using a linking file
Release Notes
Removing Cloudera AI Workbenches
Request/Response Formats (JSON)
Restarting a failed AMP setup
Restore a Cloudera AI Workbench
Restoring old data to a new workbench
Restoring to a different environment
Restrictions for upgrading R and Python packages
Role-based authorization
Role-based authorization in Model Hub
Roll back the registry upgrade
Run Code
Run workloads using a service account
Running an Experiment using MLflow
Running Models on GPU
Runtimes
Saved Images
Scala
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.12 Libraries for Workbench
Scala 2.12 Libraries for Workbench
Scala 2.12 Libraries for Workbench
Securing Applications
Securing Models
Security
September 23, 2019
September 26, 2024
September 27, 2022
Service Accounts
Set up a custom repository location
Set up a Hive or Impala data connection manually
Set up minimum permissions
Setting Permissions for an Experiment
Setting the workload password
Setting up a Custom Data Connection
Setting up a data connection to Cloudera Data Hub
Setting up a Spark data connection
Setting up access for Cloudera AI Registry in a non-RAZ-enabled environment
Setting up access for Cloudera AI Registry in a RAZ-enabled environment
Setting up Amazon S3 data connection
Setting up an HTTP Proxy for Spark 2
Setting up Cloudera AI Registry
Setting up Cloudera AI Workbenches for high volume Workloads
Setting up VS Code
Setup Data Lake Access
Sharing Job and Session Console Outputs
Simple Plots
Site Administration
Spark
Spark configuration files
Spark Log4j Configuration
Spark on Cloudera AI
Spark on Kubernetes
Spark web UIs
SSH Keys
Starting Data Discovery and Visualization
Starting sessions and creating SSH endpoints
Stop a Session
Supported Model Artifact Formats
Suspend and resume Cloudera AI Workbenches
Sync machine users from the Synced team
Synchronizing Cloudera AI Registry with a workbench
Tagging disks to avoid garbage collection
Team key
Technical metrics for Models
Terminology
Test LDAP Configuration
Testing a browser-based IDE in a Session
Testing calls to a Model
Testing GPU Setup
Testing ML Runtime GPU Setup
Testing ML Runtime GPU Setup
Third-party Editors
Top Tasks
Top Tasks for Cloudera AI
Tracked User events
Tracking metrics for deployed models
Tracking model metrics without deploying a model
Training the Model
Troubleshooting
Troubleshooting Cloudera AI Workbenches on AWS
Troubleshooting Cloudera AI Workbenches on Azure
Troubleshooting issues with Cloudera AI Registry API
Troubleshooting Issues with Workloads
Troubleshooting Kerberos issues
Troubleshooting Spark issues
Troubleshooting: 401 Unauthorized
Troubleshooting: 401 Unauthorized when accessing Hive
Troubleshooting: Empty data page
Troubleshooting: Existing connection name
Troubleshooting: Some connections not shown
Tuning auto-scaling sensitivity using the API
Turning off ML Runtimes Addons
Updating Cloudera AI Registry configuration
Upgrade Cloudera AI Registry
Upgrading Cloudera AI Workbenches
Upload and work with local files
Usage guidelines for deploying models with Cloudera AI
Use a non-transparent proxy with Cloudera AI on AWS environments
Use Direct Reader Mode with PySpark
Use Direct Reader Mode with SparklyR
Use JDBC Connection with PySpark
User Defined Routing (UDR)
User Roles
User Roles
User Roles and Team Accounts
Using an MLflow Model Artifact in a Model REST API
Using Cloudera AI Inference service
Using Cloudera AI Registry
Using Cloudera Copilot
Using Conda Runtime
Using Conda to manage dependencies
Using data connection snippets
Using Editors for ML Runtimes
Using GPUs for Cloudera AI projects
Using Heuristic-based metrics
Using JupyterLab with ML Runtimes
Using LLM-as-a-Judge metrics
Using ML Runtimes Addons
Using ML Runtimes with cdswctl
Using MLflow SDK to register customized models
Using Model Hub
Using Registered Models
Using Runtime Catalog
Using Spark 2 from Python
Using Spark 2 from Scala
Using Spark 3 from R
Using the REST Client
View replica logs for a model
Viewing details for Cloudera AI Registries
Viewing Details for Cloudera AI Registry
Viewing details of a Cloudera AI Inference service instances using the UI
Viewing details of a Model Endpoint using UI
Viewing details of a registered model
Viewing Job History
Viewing lineage for a model deployment in Atlas
Viewing registered model information
Visualizations
Visualizing Experiment Results
Web session timeouts
What's New
What's New in ML Runtimes older releases
What's New in ML Runtimes version 2024.02.1
What's New in ML Runtimes version 2024.05.1
What's New in ML Runtimes version 2024.05.2
What's New in ML Runtimes version 2024.10.1
What's new in ML Runtimes version 2025.01.1
What's new in ML Runtimes version 2025.01.2
What's new in ML Runtimes version 2025.01.3
Workbench backup and restore prerequisites
Workbench editor file types
Worker Network Communication
Workers API
Workflows for active Models
Working with Data Discovery and Visualization

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Filter topics

Securing Models

Cloudera AI Project Lifecycle
▶︎Models
1. Models - Concepts and Terminology
▶︎Challenges with Machine Learning in production
▶︎Using Cloudera AI Registry
Creating and deploying a Model
Usage guidelines for deploying models with Cloudera AI
Known Issues and Limitations with Model Builds and Deployed Models
Request/Response Formats (JSON)
Testing calls to a Model
▼Securing Models
1. Access Keys for Models
2. ▶︎API Key for Models
Workflows for active Models
Technical metrics for Models
Debugging issues with Models
Deleting a Model
Configuring model request payload size
▶︎Managing Cloudera Copilot
▶︎Example - Model training and deployment (Iris)
1. Training the Model
2. Deploying the Model

»

Securing Models

You can secure your Cloudera AI models using Access keys or API keys.

important

Cloudera on cloud allows customers to maintain full ownership and control of their data and workloads and is designed to operate in some of the most restricted on cloud environments. Since Cloudera on cloud runs in a customer’s cloud account, Security and Compliance is a shared responsibility between Cloudera and its on cloud customers. For more information, see Cloudera’s Shared Responsibility Model.

Related information

Cloudera’s Shared Responsibility Model

Access Keys for Models
Each model in Cloudera AI has a unique access key associated with it. This access key is a unique identifier for the model.
API Key for Models
You can prevent unauthorized access to your models by specifying an API key in the Authorization header of your model HTTP request. This topic covers how to create, test, and use an API key in Cloudera AI.

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