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▶︎Cloudera
1. Reference Architectures
▶︎Cloudera Public Cloud
▼Cloudera Private Cloud
▶︎Cloudera Manager
1. Cloudera Manager
▶︎Applications
▶︎Legacy
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Data Services
Getting Started
Cloudera Manager
Management Console
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Data Engineering
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CDW Runtime
Machine Learning
Base
Getting Started
Runtime & Cloudera Manager
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▶︎All roles
▼Data Scientist
▶︎Administrator
1. ▶︎Managing your CML Workspace service
2. ▶︎Supporting your CML service
  1. ▶︎Troubleshooting
    1. Recommended Troubleshooting Workflow
    2. Downloading diagnostic bundles for a workspace

(Optional) Configure the Sync Between Cloudera Machine Learning 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
About CML
Access Keys for Models
Access Keys for Models
Access the Terminal
Accessing data with Spark
Accessing Environmental Variables from Projects
Accessing Environmental Variables from Projects
Accessing local files in Ozone
Accessing Ozone
Accessing Ozone from Spark
Active Model Workflows
Add a catalog
Add Cloudera Machine Learning as an Interpreter for PyCharm
Add Docker registry credentials
Add Docker registry credentials and certificates
Adding a Collaborator
Adding an SSH Key to GitHub
Adding Hadoop CLI to ML Runtime Sessions
Adding New ML Runtimes
Adding Project Collaborators
Adding Spark to ML Runtime Sessions
Administrator
All roles
AMP Project Specification
Analytical Applications
Analytical Applications
Apache Spark supported versions
API
API Key for Models
API Key for Models
API v2 Usage
Application Polling Endpoint
Applications
Applied ML Prototypes (AMP)
Applied ML Prototypes (AMPs)
Automatic Logging
Autoscaling Workloads with Kubernetes
Back up an ML workspace
Backing up ML workspaces
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
Basic Concepts and Terminology
Build and deployment commands for models
Build Image
Build the New Docker Image
Build the New Docker Image
Built-in CML Visualizations
Built-in CML Visualizations
Business Users and CML
Catalog File Specification
cdswctl - CLI Client
cdswctl command reference
Challenges with Machine Learning in production
Challenges with model deployment and serving
Challenges with model governance
Challenges with model monitoring
Choosing Default Engine
CLI
Cloudera Data Visualization for ML
Cloudera Data Visualization for ML
Cloudera Machine Learning Overview
Cloudera Machine Learning requirements (ECS)
Cloudera Machine Learning requirements (OCP)
CML and outbound network access
CML API v2
CML Experiment Tracking through MLflow API
Collaboration models
Collaboration Models
Command Line Tools in CML
Configuration Options
Configure a Browser IDE as an Editor
Configure a Browser IDE at the Project Level
Configure a Local IDE using an SSH Gateway
Configure PyCharm as a Local IDE
Configure PyCharm to use Cloudera Machine Learning as the Remote Console
Configure VS Code as a Local IDE
Configuring Email with SMTP
Configuring External Authentication with LDAP and SAML
Configuring HTTP Headers for Cloudera Machine Learning
Configuring Project-level Runtimes
Configuring Quotas
Configuring SAML Authentication
Configuring the Engine Environment
Connect to CDW
Connect to external Amazon S3 buckets
Connect to External SQL Databases
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 an Ozone data connection
Creating a Customized Engine Image
Creating a Job
Creating a Model Registry
Creating a model using MLflow
Creating a new version of a Model Registry
Creating a Pipeline
Creating a project from a password-protected Git repo
Creating a Project with Legacy Engine Variants
Creating a Project with Legacy Engine Variants
Creating a Team
Creating an SSH Tunnel
Creating and Deploying a Model
Creating and Deploying a Model
Creating Customized ML Runtimes
Creating New AMPs
Creating Resource Profiles
Creating Resource Profiles
Custom AMP Catalog
Custom Runtime Addons with CML
Custom Template Projects
Customized Engine Images
Customized Runtimes
Data Access
Data Access
Data Scientist
Debugging Issues with Experiments
Debugging Issues with Models
Deleting a Model
Deleting a Project
Deleting Model Registry
Deploy a new model with updated resources
Deploy an ML Workspace with Support for TLS
Deploy an ML Workspace with Support for TLS on ECS
Deploy the Model
Deploying a model from the destination Project page
Deploying a model from the Model Registry page
Deploying an MLflow model as a CML Model REST API
Differences Between Public and Private Cloud
Disable Addons
Disable or Deprecate Runtime Addons
Disabling Model Registry
Disabling Runtimes
Distribute the Image
Distribute the Image
Distributed Computing
Distributed Computing with Workers
Dockerfile compatible with PBJ Workbench
Documenting Your Analysis
Documenting Your Analysis
Download and Configure cdswctl
Download cdswctl and Add an SSH Key
Downloading diagnostic bundles for a workspace
Editor Customization
Editors
Embedded Web Applications
Enable Cross-Origin Resource Sharing (CORS)
Enable HTTP Security Headers
Enable HTTP Strict Transport Security (HSTS)
Enabling authentication
Enabling authentication
Enabling model governance
Enabling model metrics
End-to-End Example: MeCab
Engine Dependencies
Engine Environment Variables
Engine Environment Variables
Engines for Experiments & Models
Environmental Variables
Environmental Variables
Ephemeral storage
Example - Model Training and Deployment (Iris)
Example models with PBJ Runtimes
Example: A Shiny Application
Example: Locating and Adding JARs to Spark 2 Configuration
Example: Montecarlo Estimation
Experiments
Experiments
Experiments - Concepts and Terminology
Experiments with MLflow
Export Usage List
Generating an API key
Generating an API key
Get started with CML on Private Cloud
Git for Collaboration
GPU node setup
GPUs
Grid Displays
Grid Displays
Hadoop Authentication for ML Workspaces
Host names required by AMPs
How to make base cluster configuration changes
How to upgrade CML workspaces
HTML Visualizations
HTML Visualizations
IFrame Visualizations
IFrame Visualizations
Including Images in allowlist for Cloudera Machine Learning projects
Initialize an SSH Connection to Cloudera Machine Learning for VS Code
Initialize an SSH Endpoint
Installing a Jupyter extension
Installing a Jupyter kernel
Installing a non-transparent proxy in a CML environment
Installing Additional ML Runtimes Packages
Installing Additional Packages
Internal Network File System on ECS
Internal Network File System on OCP
Introduction to Private Cloud
Jobs and Pipelines
Jobs API
Jupyter Magic Commands
Jupyter Magics
Kerberos Errors
Known Issues
Known issues and limitations
Known Issues and Limitations
Launch a Session
Legacy Engine Level Configuration
Legacy Engines
Limitations
Limitations
Limitations
Limitations
Limitations on Private Cloud
Limiting files in Explorer view
Linking an Existing Project to a Git Remote
Log into cdswctl
Machine Learning Project Lifecycle
Managing a Team Account
Managing API Keys
Managing API Keys
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 Project Files
Managing Projects
Managing Projects
Managing Users
Managing your Personal Account
Metadata for Custom ML Runtimes
ML Governance Requirements
ML Runtimes 2020.11
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 2021.12
ML Runtimes 2022.04
ML Runtimes 2022.04
ML Runtimes 2022.11
ML Runtimes 2022.11
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 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 versus Legacy Engine
ML Runtimes versus Legacy Engine
ML Runtimes What's New
ML Workspaces
Model explainability, interpretability, and reproducibility
Model Governance
Model governance using Apache Atlas
Model Metrics
Model visibility
Models
Models
Models - Concepts and Terminology
Modes of Configuration
Modifying Project Settings
Monitoring Active Models
Monitoring and Alerts
Monitoring applications
Monitoring ML Workspaces
Monitoring User Activity
Monitoring User Events
Native Workbench Console and Editor
Network File System (NFS)
NFS Options for Private Cloud
Onboarding Business Users
PBJ R 3.6 Libraries
PBJ R 4.0 Libraries
PBJ R 4.1 Libraries
PBJ Runtimes and Models
PBJ Workbench
Personal Key
Pre-Installed Packages in Engines
Pre-Installed Packages in ML Runtimes
Pre-Installed Packages in ML Runtimes
Prepare to manage models using the model CLI
Project Garbage Collection
Provision an ML Workspace
Python
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 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 3.7.9 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 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 3.8.6 Libraries for JupyterLab
Python 3.8.6 Libraries for Workbench
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 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
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
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
Registering a model using MLflow SDK
Registering a model using the Model Registry user interface
Registering and deploying a Model Registry
Registering training data lineage using a linking file
Release Notes
Removing ML Workspaces
Replace a Certificate
Request/Response Formats (JSON)
Requirements
Restore an ML workspace
Restricting User-Controlled Kubernetes Pods
Restrictions for upgrading R and Python packages
Run Code
Run Experiment / Deploy Model
Running an Experiment (Legacy)
Running an Experiment using MLflow
Running Spark with Yarn on the CDP base cluster
Runtimes
Saved Images
Saved Images
Saving Files
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
Securing Applications
Securing Models
Securing Models
Securing Models
Security
Set up a custom repository location
Setting Permissions for an Experiment
Setting Up an HTTP Proxy for Spark 2
Setting up Model Registry
Setting up VS Code
Sharing Job and Session Console Outputs
Simple Plots
Simple Plots
Site Administration
Snapshot Code
Spark
Spark Configuration Files
Spark Log4j Configuration
Spark on CML
Spark Web UIs
SSH Keys
Stop a Session
Team Key
Technical Metrics for Models
Test your connectivity to the CDP-DC cluster
Testing a Browser IDE in a Session
Testing Calls to a Model
Testing GPU Setup
Testing ML Runtime GPU Setup
Third-Party Editors
Top Tasks
Tracked User Events
Tracking Metrics
Tracking metrics for deployed models
Tracking model metrics without deploying a model
Train the Model
Troubleshooting
Troubleshooting
Troubleshooting Issues with Workloads
Turning off ML Runtimes Addons
Upgrading CML workspaces version 1.4.1 to 1.5.0 on ECS
Upgrading CML workspaces version 1.4.1 to 1.5.0 on OCP
Upload and work with local files
Usage Guidelines
Use JDBC Connection with PySpark
User Roles
User Roles and Team Accounts
Using an External NFS Server
Using an MLflow Model Artifact in a Model REST API
Using Conda Runtime
Using Conda to Manage Dependencies
Using Editors for ML Runtimes
Using GPUs for Cloudera Machine Learning projects
Using JupyterLab with ML Runtimes
Using ML Runtimes Addons
Using Model Registry
Using Runtime Catalog
Using Spark 2 from Python
Using Spark 2 from R
Using Spark 2 from Scala
View replica logs for a model
Viewing Details for Model Registry
Viewing Details for Model Registry
Viewing Job History
Viewing lineage for a model deployment in Atlas
Visualizations
Visualizing Experiment Results
Web session timeouts
What's New
Workbench editor file types
Worker Network Communication
Workers API
Workspace Backup and Restore Prerequisites

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Filter topics

Securing Models

Machine Learning Project Lifecycle
▶︎Experiments
1. Experiments - Concepts and Terminology
▶︎Models
1. Models - Concepts and Terminology
▶︎Challenges with Machine Learning in production
▶︎Using Model Registry
1. ▶︎Setting up Model Registry
2. ▶︎Registering and deploying a Model Registry
Creating and Deploying a Model
Usage Guidelines
Known Issues and Limitations
Request/Response Formats (JSON)
Testing Calls to a Model
▼Securing Models
1. Access Keys for Models
2. ▶︎API Key for Models
Active Model Workflows
Technical Metrics for Models
Debugging Issues with Models
Deleting a Model
▶︎Example - Model Training and Deployment (Iris)
1. Train the Model
2. Deploy the Model

»

Models

Securing Models

Access Keys for Models
Each model in Cloudera Machine Learning 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 Machine Learning.

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1.5
1.4
1. 1.4.1
2. 1.4.0