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# Version 7.2.1 {: #version-721 }
_September 19, 2021_
The DataRobot v7.2.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.2.0 release notes for:
* [Features introduced in v7.2.0](v7.2.0-aml)
## Issues fixed in v7.2.1 {: #issues-fixed-in-v721 }
The following issue... | v7.2.1-aml |
# Version 7.2.8 {: #version-728 }
_January 26, 2022_
The DataRobot v7.2.8 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.2.0 release notes for:
* [Features introduced in v7.2.0](v7.2.0-aml)
## Issues fixed in v7.2.8 {: #issues-fixed-in-v728 }
The following issues ... | v7.2.8-aml |
---
title: Version 7.2.0 maintenance releases
description: Maintenance releases for the DataRobot Release 7.2 major release.
---
# Version 7.2.x maintenance releases {: #version-72x-maintenance-releases }
The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform plat... | index |
# Version 7.2.6 {: #version-726 }
_November 23, 2021_
The DataRobot v7.2.6 release does not include any customer-impacting issues. See the v7.2.0 release notes for:
* [Features introduced in v7.2.0](v7.2.0-aml)
## Updated language localization {: #updated-language-localization }
Localization of the documentation h... | v7.2.6-aml |
# Version 7.2.2 {: #version-722 }
_September 28, 2021_
The DataRobot v7.2.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.2.0 release notes for:
* [Features introduced in v7.2.0](v7.2.0-aml)
## Issues fixed in v7.2.2 {: #issues-fixed-in-v722 }
The following issue... | v7.2.2-aml |
# Version 7.3.5 {: #version-735 }
_February 18, 2022_
The DataRobot v7.3.5 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:
* [Features introduced in v7.3.0](v7.3.0-aml)
## Updated language localization {: #updated-language-localization }
Loca... | v7.3.5-aml |
# Version 7.3.3 {: #version-733 }
_January 17, 2022_
The DataRobot v7.3.3 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:
* [Features introduced in v7.3.0](v7.3.0-aml)
## Issues fixed in v7.3.3 {: #issues-fixed-in-v733 }
The following issues ... | v7.3.3-aml |
# Version 7.3.1 {: #version-731 }
_December 16, 2021_
The DataRobot v7.3.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:
* [Features introduced in v7.3.0](v7.3.0-aml)
## Issues fixed in v7.3.1 {: #issues-fixed-in-v731 }
The following issues... | v7.3.1-aml |
# Version 7.3.4 {: #version-734 }
_February 7, 2022_
The DataRobot v7.3.4 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:
* [Features introduced in v7.3.0](v7.3.0-aml)
## Issues fixed in v7.3.4 {: #issues-fixed-in-v734 }
The following issues ... | v7.3.4-aml |
---
title: Version 7.3.x maintenance releases
description: Maintenance releases for the DataRobot Release 7.3 major release.
---
# Version 7.3.x maintenance releases {: #version-73x-maintenance-releases }
The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform platf... | index |
# Version 7.3.6 {: #version-736 }
_May 4, 2022_
The DataRobot v7.3.6 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:
* [Features introduced in v7.3.0](v7.3.0-aml)
## Issues fixed in v7.3.6 {: #issues-fixed-in-v736 }
The following issues have ... | v7.3.6-aml |
# Version 7.3.2 {: #version-732 }
_December 21, 2021_
The DataRobot v7.3.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:
* [Features introduced in v7.3.0](v7.3.0-aml)
## Issues fixed in v7.3.2 {: #issues-fixed-in-v732 }
The following issues... | v7.3.2-aml |
# Version 7.1.4 {: #version-714 }
_December 21, 2021_
The DataRobot v7.1.4 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.1.0 release notes for:
* [Features introduced in v7.1.0](v7.1.0-aml)
## Issues fixed in v7.1.4 {: #issues-fixed-in-v714 }
The following issues... | v7.1.4-aml |
# Version 7.1.2 {: #version-712 }
_August 2, 2021_
The DataRobot v7.1.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.1.0 release notes for:
* [Features introduced in v7.1.0](v7.1.0-aml)
## Updated language localization {: #updated-language-localization }
Localiz... | v7.1.2-aml |
---
title: Version 7.1.x maintenance releases
description: Maintenance releases for the DataRobot Release 7.1 major release.
---
# Version 7.1.x maintenance releases {: #version-71x-maintenance-releases }
The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform pla... | index |
# Version 7.1.1 {: #version-711 }
_June 19, 2021_
The DataRobot v7.1.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.1.0 release notes for:
* [Features introduced in v7.1.0](v7.1.0-aml)
## Issues fixed in v7.1.1 {: #issues-fixed-in-v711 }
The following issues hav... | v7.1.1-aml |
# Version 7.1.3 {: #version-713 }
_August 27, 2021_
The DataRobot v7.1.3 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.1.0 release notes for:
* [Features introduced in v7.1.0](v7.1.0-aml)
## Issues fixed in v7.1.3 {: #issues-fixed-in-v713 }
The following issues h... | v7.1.3-aml |
---
title: V9.0.3
description: DataRobot Release 9.0.3 release notes.
---
# V9.0.3 {: #v903 }
_June 30, 2023_
The DataRobot v9.0.3 release includes some improvements and fixed issues in the DataRobot Self-Managed AI Platform platform. See the v9.0.0 release notes for:
* [Features introduced in v9.0.0](v9.0/index)
... | v9.0.3-aml |
---
title: V9.0.1
description: DataRobot Release 9.0.1 release notes.
---
# V9.0.1 {: #v901 }
_June 6, 2023_
The DataRobot v9.0.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v9.0.0 release notes for:
* [Features introduced in v9.0.0](v9.0/index)
## Issues fixed i... | v9.0.1-aml |
---
title: V9.0 maintenance releases
description: Maintenance releases for the DataRobot Release 9.0 major release.
---
# V9.0 maintenance releases {: #v90-maintenance-releases }
The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform platform. See also the [featur... | index |
---
title: V9.0.2
description: DataRobot Release 9.0.2 release notes.
---
# V9.0.2 {: #v902 }
_June 9, 2023_
The DataRobot v9.0.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v9.0.0 release notes for:
* [Features introduced in v9.0.0](v9.0/index)
## Issues fixed i... | v9.0.2-aml |
---
title: V9.0.4
description: DataRobot Release 9.0.4 release notes.
---
# V9.0.4 {: #v904 }
_July 24, 2023_
The DataRobot v9.0.4 release includes some fixed issues in the DataRobot Self-Managed AI Platform and issues found in the installation of the v9.0.3 release. For a complete list of notes since the v9.0.3 re... | v9.0.4-aml |
# Version 7.0.2 {: #version-702 }
_April 26, 2021_
The DataRobot v7.0.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.0.0 release notes for:
* [Features introduced in v7.0.0](v7.0.0-aml)
## Updated language localization {: #updated-language-localization }
Localiz... | v7.0.2-aml |
---
title: Version 7.0.x maintenance releases
description: Maintenance releases for the DataRobot Release 7.0 major release.
---
# Version 7.0.x maintenance releases {: #version-70x-maintenance-releases }
The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform pla... | index |
# Version 7.0.1 {: #version-701 }
_March 29, 2021_
The DataRobot v7.0.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.0.0 release notes for:
* [Features introduced in v7.0.0](v7.0.0-aml)
## Issues fixed in v7.0.1 {: #issues-fixed-in-v701 }
The following issues ha... | v7.0.1-aml |
# Version 7.0.3 {: #version-703 }
_December 21, 2021_
The DataRobot v7.0.3 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.0.0 release notes for:
* [Features introduced in v7.0.0](v7.0.0-aml)
## Issues fixed in v7.0.3 {: #issues-fixed-in-v703 }
The following issues... | v7.0.3-aml |
---
title: Build models
description: Introduces the phases of building models, including Advanced options, building models, and managing projects.
---
# Build models {: #build-models }
These sections describe aspects of preparing to build, building, and managing models and projects:
Topic | Describes...
----- | ---... | index |
---
title: Out-of-time validation modeling
description: Out-of-time validation (OTV) is a method of modeling time-relevant data using date/time partitioning.
---
# Out-of-time validation (OTV) {: #out-of-time-validation-otv }
Out-of-time validation (OTV) is a method for modeling time-relevant data. With OTV you are ... | otv |
---
title: Text AI resources
description: Provides links to Text AI resources available in DataRobot.
---
# Text AI resources {: #text-ai-resources }
Text AI in DataRobot allows you to seamlessly incorporate text data into your model without being a Natural Language Processing (NLP) expert and without injecting extr... | textai-resources |
---
title: Specialized workflows
description: Leverage the support for alternative workflows for specialized data types such as anomaly detection, multilabel modeling, Visual and Location AI, and date/time partitioning.
---
# Specialized workflows {: #specialized-workflows }
The following sections describe alternati... | index |
---
title: Multilabel modeling
description: In DataRobot's multilabel modeling, each row in a dataset is associated with one, several, or zero labels.
---
# Multilabel modeling {: #multilabel-modeling }
!!! info "Availability information"
Availability of multilabel modeling is dependent on your DataRobot package. If... | multilabel |
---
title: Quantile regression analysis
description: For some projects, predicting the tendency (average or median, for example) of the target variable is not the prime concern; some are more interested in predicting a conditional value (a quantile).
section_name: AutoML
maturity: public-preview
---
# Quantile regres... | quantile-reg |
---
title: Blueprint editor detailed view
description: Toggle between a summary view and a detailed view when working with the Composable ML blueprint editor.
section_name: AutoML
maturity: public-preview
---
# Blueprint editor detailed view {: #blueprint-editor-detailed-view }
!!! info "Availability information"
... | blueprint-toggle |
---
title: AutoML public preview features
description: Read preliminary documentation for AutoML features currently in the DataRobot public preview pipeline.
section_name: AutoML
maturity: public-preview
---
# AutoML public preview features {: #automl-public-preview-features }
{% include 'includes/pub-preview-notice-... | index |
---
title: GPUs for deep learning
description: Use GPU workers for faster training.
section_name: AutoML
maturity: public-preview
---
# GPUs for deep learning {: #gpus-for-deep-learning }
!!! info "Availability information"
GPU workers are disabled by default. Contact your DataRobot representative or administrato... | gpus |
---
title: Prediction Explanations for clusters
description: Understand the reasons behind a clustering model’s outcomes using Prediction Explanations to uncover the factors that most contribute to those outcomes.
section_name: AutoML
maturity: public-preview
---
# Prediction Explanations for clusters {: #prediction-e... | cluster-pe |
---
title: Configure hyperparameters for custom tasks
description: Define the hyperparameters for a custom task.
section_name: AutoML
maturity: public-preview
---
# Configure hyperparameters for custom tasks {: #configure-hyperparameters-for-custom-tasks }
!!! info "Availability information"
Hyperparameters for c... | cml-hyperparam |
---
title: Model insights
description: Introduces the many insights the DataRobot Leaderboard provides when you select a model, with links to details.
---
# Model insights {: #model-insights }
When you select a model, DataRobot makes available a large selection of insights, grouped by purpose, appropriate for that m... | index |
---
title: External prediction comparison
description: Compare model predictions created outside of DataRobot with DataRobot-driven predictions to drive the best business decisions.
---
# External prediction comparison {: #external-prediction-comparison }
For organizations that have existing supervised [time series](... | cyob |
---
title: Segmented modeling
description: Describes segmented modeling for multiseries projects in DataRobot.
---
# Segmented modeling for multiseries {: #segmented-modeling-for-multiseries }
Complex and accurate demand forecasting typically requires deep statistical know-how and lengthy development projects around... | ts-segmented |
---
title: Clustering
description: Available for time series projects, clustering groups by similar series across a multiseries dataset for insights or to prepare for segmented modeling.
---
# Clustering {: #clustering }
Time series clustering is an out of the box solution unique to DataRobot that enables you to eas... | ts-clustering |
---
title: Multiseries modeling
description: Multiseries modeling allows you to model datasets that contain multiple time series based on a common set of input features.
---
# Multiseries modeling {: #multiseries-modeling }
!!! note
See these additional [date/time partitioning considerations](ts-consider#datetime... | multiseries |
---
title: Time series modeling
description: Follow the steps used to create time series models.
---
# Time series modeling {: #time-series-modeling }
!!! info "Availability information"
Contact your DataRobot representative for information on enabling automated time series (AutoTS) modeling.
Time series modeli... | ts-flow-overview |
---
title: Time series predictions
description: Understand the prediction methods used with DataRobot's time series modeling.
---
# Time series predictions {: #time-series-predictions }
!!! info "Availability information"
Contact your DataRobot representative for information on enabling automated time series (Au... | ts-predictions |
---
title: Nowcasting
description: Describes making predictions for the present and very near future (very short-range forecasting).
---
# Nowcasting {: #nowcasting }
Nowcasting is a method of time series modeling that predicts the current value of a target based on past and present data. Technically, it is a foreca... | nowcasting |
---
title: Time-series modeling
description: This topic introduces components of time-aware modeling, a recommended practice for data science problems where conditions may change over time.
---
# Time-series modeling {: #time-series-modeling }
!!! info "Availability information"
Time series modeling is not curr... | index |
---
title: Time series insights
description: Describes the visualizations available to help interpret your data and models.
---
# Time series insights {: #time-series-insights}
This section describes the visualizations available to help interpret your data and models, both [prior to modeling](#prior-to-modeling) and ... | ts-leaderboard |
---
title: What is time-aware modeling?
description: Use OTV when your data is time-relevant but you are not forecasting; use time series when you want to forecast multiple future values; use nowcasting to determine an unknown current value of a time series.
---
# What is time-aware modeling? {: #what-is-time-aware-m... | whatis-time |
---
title: Modeling reference
description: Introduces sections that provide a deep dive into aspects of DataRobot functionality, including Data and sharing, Modeling details, Eureqa advanced tuning.
---
# Modeling reference {: #modeling-reference }
The topics in these section provide a deep dive into aspects of DataR... | index |
---
title: Image Augmentation
description: Describes the settings available from the Image Augmentation advanced option tab, where you can create new training images by randomly transforming existing images, thereby increasing the size of the training data.
---
# Image Augmentation {: #image-augmentation }
Train-ti... | ttia |
---
title: Clustering advanced options
description: Allows you to set the number of clusters that DataRobot automatically discovers during time series clustering.
---
# Clustering advanced options {: #clustering-advanced-options }
{% include 'includes/ts-cluster-adv-opt-include.md' %}
| time-series-cluster-adv-opt |
---
title: Feature Constraints
description: Describes the settings available from the Feature Constraints advanced option tab, where you can control the influence, both up and down, between variables and the target.
---
# Feature Constraints {: #feature-constraints }
The **Feature Constraints** tab provides the too... | feature-con |
---
title: Advanced options
description: Set advanced parameters before building models to set non-default characteristics of the model build.
---
# Advanced options {: #advanced-options }
After importing data and selecting a target variable, the **Data** page appears. From this page you can click the **Show advance... | index |
---
title: External Predictions
description: Through the **External Predictions** advanced option tab, you can bring external model(s) into the DataRobot AutoML environment, view them on the Leaderboard, and run a subset of DataRobot's evaluative insights for comparison against DataRobot models.
---
# External Predict... | external-preds |
---
title: Bias and Fairness
description: Describes the Bias and Fairness advanced option tab, where you can set protected features, choose a fairness metric, and configure bias mitigation techniques.
---
# Bias and Fairness {: #bias-and-fairness }
Bias and Fairness testing provides methods to calculate fairness for... | fairness-metrics |
---
title: Partitioning
description: Describes the settings available from the Partitioning advanced option tab, where you can set the method DataRobot uses to group observations (or rows) together for evaluation and model building.
---
# Partitioning {: #partitioning }
DataRobot provides a mechanism to select the p... | partitioning |
---
title: Time series
description: Describes the settings available from the Time Series advanced option tab, where you can set features known in advance, exponential trends, and differencing for time series projects.
---
# Time Series {: #time-series }
{% include 'includes/ts-adv-opt-include.md' %} | time-series-adv-opt |
---
title: Smart Downsampling
description: Smart Downsampling is a technique to reduce total dataset size by reducing the size of the majority class, enabling you to build models faster without sacrificing accuracy.
---
# Smart Downsampling {: #smart-downsampling }
Smart downsampling is a technique to reduce total da... | smart-ds |
---
title: Additional
description: Describes the settings available from the Additional advanced option tab, where you can fine-tune a variety of aspects of model building.
---
# Additional {: #additional }
From the **Additional** tab you can fine-tune a variety of aspects of model building, with the options depende... | additional |
---
title: Frozen runs
description: Describes “frozen runs,” a setting that freezes parameter settings from a model’s early, small sample size-based run to improve build times for ever increasing sample sizes.
---
# Frozen runs {: #frozen-runs }
To tune model performance on a sample, DataRobot systematically applies... | frozen-run |
---
title: Feature lists
description: Describes how to work with feature lists, which control the subset of features that DataRobot uses to build models.
---
# Feature lists {: #feature-lists }
<em>Feature lists</em> control the subset of features that DataRobot uses to build models. You can use one of the [automat... | feature-lists |
---
title: Basic model workflow
description: Describes the basic workflow of the DataRobot model building process, with links to complete documentation for each step.
---
# Basic model workflow {: #basic-model-workflow }
Once the import has finished, DataRobot displays the **Data** page. From here you can set a targ... | model-data |
---
title: Model Repository
description: DataRobot's library of algorithms used to build models. They may be run as part of Autopilot and also are available for manual selection.
---
# Model Repository {: #model-repository }
The Repository is a library of modeling blueprints available for a selected project. These bl... | repository |
---
title: Add/delete models
description: Describes how to work with models after your initial model build, including creating blenders.
---
# Add/delete models {: #adddelete-models }
This section describes how to work with models after your initial model build.
## Add models from the Leaderboard {: #add-models-fr... | creating-addl-models |
---
title: Build models
description: This topic introduces elements of the basic modeling workflow as well as methods for building additional models in a project.
---
# Build models {: #build-models }
These sections describe elements of the basic modeling workflow as well as methods for building additional models in... | index |
---
title: Unlock Holdout
description: Holdout, the portion of your data DataRobot reserves when building models, provides an evaluation metric that measures a model's accuracy against the unseen data to validate model quality.
---
# Unlock Holdout {: #unlock-holdout }
The *Holdout* column displays an evaluation me... | unlocking-holdout |
---
title: Comprehensive Autopilot
description: Describes the model building mode that returns the most accurate models by running all repository modeling blueprints on the maximum Autopilot sample size.
---
# Comprehensive Autopilot {: #comprehensive-autopilot }
It is a common decision when building models to prior... | more-accuracy |
---
title: Clustering
description: Learn how to use clustering, a form of unsupervised learning, to separate your samples into clusters that help you to better understand your data or to use as segments for time series modeling.
---
# Clustering {: #clustering }
Clustering, an application of [unsupervised learning](... | clustering |
---
title: Unsupervised learning
description: Work with unlabeled data to build models in unsupervised mode (anomaly detection and clustering).
---
# Unsupervised learning {: #unsupervised-learning }
Typically DataRobot works with labeled data, using supervised learning methods for model building. With supervised le... | index |
---
title: Anomaly detection
description: Work with unlabeled data to build models in unsupervised mode (anomaly detection).
---
# Anomaly detection {: #anomaly-detection }
DataRobot works with unlabeled data (or [partially labeled](#partially-labeled-data) data) to build anomaly detection models. Anomaly detection,... | anomaly-detection |
---
title: Composable ML overview
description: Composable ML provides a full-flexibility approach to model building, allowing you to build blueprints that best suit your needs using built-in tasks and custom Python/R code.
---
# Composable ML overview {: #composable-ml-overview }
Composable ML provides a full-flexib... | cml-overview |
---
title: Custom environments
description: Describes how to build a custom environment when a custom task requires something not contained in one of DataRobot's built-in environments.
---
# Custom environments {: #custom-environments }
Once uploaded into DataRobot, [custom tasks](cml-custom-tasks) run inside of env... | cml-custom-env |
---
title: Composable ML
description: Documentation for Composable Machine Learning (ML), including a Quickstart, overview, and instructions for editing or crating blueprints, tasks, and environments.
---
# Composable ML {: #composable-ml }
Documentation for Composable Machine Learning (ML) includes a Quickstart, ov... | index |
---
title: Modify a blueprint
description: Describes how a blueprint works and and the basics of using the blueprint editor.
---
# Modify a blueprint {: #modify-a-blueprint }
This section describes the blueprint editor. A blueprint represents the high-level end-to-end procedure for fitting the model, including any p... | cml-blueprint-edit |
---
title: DRUM CLI tool
description: DataRobot Model Runner (DRUM) is a tool that allows you to work with Python, R, and Java custom models and to quickly test custom tasks.
---
{% include 'includes/drum-tool.md' %}
{% include 'includes/drum-for-ubuntu.md' %}
{% include 'includes/drum-for-mac.md' %}
### Use DRUM ... | cml-drum |
---
title: Create custom tasks
description: Describes how to create and apply custom tasks and work with the resulting custom blueprints.
---
# Create custom tasks {: #create-custom-tasks }
!!! info "Availability information"
Custom tasks (also referred to as custom code) are not supported for Self-Managed AI Pl... | cml-custom-tasks |
---
title: Composable ML Quickstart
description: The Quickstart provides an example that walks you through testing and learning Composable ML so that you can then apply it against your own use case.
---
# Composable ML Quickstart {: #composable-ml-quickstart }
Composable ML gives you full flexibility to build a cust... | cml-quickstart |
---
title: Model insights
description: Visual AI provides several tools to help visually assess, understand, and evaluate model performance.
---
# Visual AI model insights {: #visual-ai-model-insights }
Visual AI provides several tools to help visually assess, understand, and evaluate model performance:
* [Image em... | vai-insights |
---
title: Visual AI predictions
description: There are a variety of methods for making predictions from DataRobot's image models; use Base64 encoding and sample scripts to simplify.
---
# Visual AI predictions {: #visual-ai-predictions }
There are various methods for making predictions from image models:
Method | D... | vai-predictions |
---
title: Visual AI overview
description: Working with image features in DataRobot follows the same workflow as that of non-image projects, with DataRobot automating the preparation, selection, and training of a wide variety of deep learning models.
---
# Visual AI overview {: #visual-ai-overview }
Working with imag... | vai-overview |
---
title: Tune models
description: Use the information gained from Visual AI insights to calibrate tuning, apply different augmentation strategies, change the neural net, and more.
---
# Tune models {: #tune-models }
Using the information gained from Visual AI [insights](vai-insights), you may decide to calibrate tu... | vai-tuning |
---
title: Visual AI
description: This topic introduces the workflow and reference materials for including images as part of your DataRobot project.
---
# Visual AI {: #visual-ai }
These sections describe the workflow and reference materials for including images as part of your DataRobot project.
Topic | Describes.... | index |
---
title: Build Visual AI models
description: Building Visual AI models, as with any DataRobot project, starts with preparing and uploading data.
---
# Build Visual AI models {: #build-visual-ai-models }
As with any DataRobot project, building Visual AI models involves preparing and uploading data:
1. [Preparing th... | vai-model |
---
title: Exploratory Spatial Data Analysis (ESDA)
description: Location AI provides a variety of tools for conducting ESDA within the DataRobot AutoML environment.
---
# Exploratory Spatial Data Analysis (ESDA) {: #exploratory-spatial-data-analysis-esda }
DataRobot Location AI provides a variety of tools for conduc... | lai-esda |
---
title: Modeling
description: When a spatial structure is present in the input dataset, Location AI’s modeling enhancements expand traditional automated feature engineering and improve model options.
---
# Modeling {: #modeling }
When a spatial structure is present in the input dataset, Location AI’s modeling enha... | lai-model |
---
title: Accuracy Over Space
description: Location AI insights help to discover spatial patterns in prediction errors and visualize prediction errors across data partitions on a map visualization.
---
# Accuracy Over Space {: #accuracy-over-space }
To assess model fidelity in a spatial setting, Location AI adds pow... | lai-insights |
---
title: Location AI
description: DataRobot Location AI adds tools and support for geospatial analysis across the entire AutoML workflow.
---
# Location AI {: #location-ai }
DataRobot Location AI adds support for geospatial analysis across the entire AutoML workflow. These tools and techniques help users improve t... | index |
---
title: Data ingest
description: DataRobot Location AI enables tapping into existing geospatial data sources through a variety of pathways.
---
# Data ingest {: #data-ingest }
DataRobot Location AI enables tapping into existing geospatial data sources through a variety of pathways, including:
* Native geospatial ... | lai-ingest |
---
title: Bias and Fairness reference
description: The Bias and Fairness feature calculates fairness for a machine learning model and identifies any biases from the model's predictive behavior.
---
# Bias and Fairness reference {: #bias-and-fairness-reference }
This section defines terminology that is commonly used... | bias-ref |
---
title: Bias and Fairness overview
description: Provides a high-level of overview of bias in machine learning and DataRobot's detection and prevention tools.
---
# Bias and Fairness overview {: #bias-and-fairness-overview }
In DataRobot, bias represents the difference between a model's predictions for different p... | bias-overview |
---
title: Bias and Fairness resources
description: Provides links to bias and fairness resources used in DataRobot.
---
# Bias and Fairness resources {: #bias-and-fairness-resources }
The tools of the Bias and Fairness feature test your models for bias. This allows you identify bias before (or after) models are dep... | index |
# Composable ML reference {: #composable-ml-reference }
Topic | Describes...
----- | ------------
[Blueprints in the AI Catalog](cml-catalog) | How to save, edit, share, and re-use blueprints from the AI Catalog.
[Validation schema](cml-validation) | How to define the expected input and output data requirements for a ... | index |
---
title: Sentiment analysis example
description: Apply DataRobot's Composable ML to capture sentiment from text.
---
# Sentiment analysis example {: #sentiment-analysis-example }
The model in this example includes reviews or tweets. The goal is to get an uplift for the model by capturing the sentiment in the text.... | cml-sentiment-example |
---
title: Blueprints in the AI Catalog
description: How to save, edit, share, and re-use blueprints from the AI Catalog.
---
# Blueprints in the AI Catalog {: #blueprints-in-the-ai-catalog }
When Composable ML is enabled, you can save blueprints to the AI Catalog. From the catalog, a blueprint can be edited, used t... | cml-catalog |
---
title: Model metadata and validation schema
description: How to use the model-metadata.yaml file to specify additional information about a custom task or a custom inference model.
---
# Model metadata and validation schema {: #model-metadata-and-validation-schema }
The `model-metadata.yaml` file is used to speci... | cml-validation |
---
title: Composable ML considerations
description: Platform support and considerations for working with DataRobot's Composable ML.
---
# Composable ML considerations {: #composable-ml-considerations }
Consider the following when working with Composable ML.
### Environment support {: #environment-support }
Compos... | cml-consider |
---
title: Use case examples
description: Sample use cases for how and when to use train-time image augmentation in image datasets.
---
# Use case examples {: #use-case-examples }
Below are some example use cases to help illustrate how you might leverage domain knowledge of your dataset to craft a beneficial augmenta... | ttia-examples |
---
title: Transformations and lists
description: To simplify comparing multiple augmentation strategies across many models, DataRobot provides the capability to create augmentation lists.
---
# Transformations and lists {: #transformations-and-lists }
To simplify comparing multiple augmentation strategies across man... | ttia-lists |
---
title: Train-time image augmentation
description: Train-time image augmentation is a processing step in the DataRobot blueprint that creates new images for training by randomly transforming existing images.
---
# Train-time image augmentation {: #train-time-image-augmentation }
**Train-time image augmentation** i... | index |
---
title: About augmented models
description: An overview of augmented modeling and how it supports the potential for smaller overall loss by improving the generalization of models on unseen data.
---
# About augmented models {: #about-augmented-models }
By creating new images for training by randomly transforming e... | ttia-introduction |
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