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---
title: XEMP qualitative strength
description: Understand how the qualitative strength indicators for XEMP Prediction Explanations are calculated.
---
# XEMP qualitative strength {: #xemp-qualitative-strength }
[XEMP-based Prediction Explanations](xemp-pe#interpret-xemp-prediction-explanations) provide a visual i... | xemp-calc |
---
title: Export charts and data
description: DataRobot exports charts as PNG images and data as CSV files; use the export function to download this data.
---
# Export charts and data {: #export-charts-and-data }
Many of the DataRobot charts have images and data available for download. (You can also download all ch... | export-results |
---
title: Leaderboard reference
description: A reference for the tags, icons, columns, and other aspects of the DataRobot model Leaderboard
---
# Leaderboard reference {: #leaderboard-reference }
The Leaderboard provides a wealth of summary information for each model built in a project. When models complete, DataRo... | leaderboard-ref |
---
title: Model recommendation process
description: As a result of the Autopilot modeling process, the most accurate individual, non-blender model is selected and then prepared for deployment.
---
# Model recommendation process {: #model-recommendation-process }
DataRobot provides an option to set the Autopilot mode... | model-rec-process |
---
title: Optimization metrics
description: Provides a complete reference to the optimization metrics DataRobot employs during the modeling process.
---
# Optimization metrics {: #optimization-metrics }
The following table lists all metrics, with a short description, available from the [**Optimization Metric**](addi... | opt-metric |
---
title: Modeling details
description: This section introduces the model building process; data partitioning and validation; features for working with a project after model building completes, including Generate AI report, Export charts and data; and links to details on its component tasks.
---
# Modeling details {... | index |
---
title: Modeling process
description: Provides details of DataRobot's modeling process, from selecting modeling modes, interpreting data summary information, and working with missing values.
---
# Modeling process {: #modeling-process }
This section provides more detail to help understand DataRobot's initial mode... | model-ref |
---
title: Worker Queue
description: The Worker Queue is where you monitor build progress and manage the resources your projects use for training models and building insights.
---
# Worker Queue {: #worker-queue }
The <em>Worker Queue</em>, displayed in the right-side panel of the application, is a place to monitor ... | worker-queue |
---
title: SHAP reference
description: Provides reference content for understanding Shapley Values, the coalitional game theory framework by Lloyd Shapley, as used in DataRobot's SHAP Prediction Explanations.
---
# SHAP reference {: #shap-reference }
SHAP is an open-source algorithm used to address the accuracy vs. e... | shap |
---
title: Generate AI Report
description: Generate an AI Report, a high-level overview of your modeling results and insights, to communicate the most important findings of your modeling project to stakeholders.
---
# Generate AI Report {: #generate-ai-report }
Once you complete an Autopilot run, you can generate an ... | generate-ai-report |
---
title: Sliced insights
description: Using a filtered subset of the full data, DataRobot segments insights by subpopulation to provide better segment-based accuracy information.
---
# Sliced insights {: #sliced-insights }
Sliced insights provide the option to view a subpopulation of a model's data based on featur... | sliced-insights |
---
title: Data partitioning and validation
description: To maximize accuracy, DataRobot separates data into training, validation/cross-validation, and holdout data.
---
# Data partitioning and validation {: #data-partitioning-and-validation }
You should evaluate and select models using only the Validation and Cross-... | data-partitioning |
---
title: Datasets
description: The Datasets tab lists each dataset that has been added to your Use Case and provides the ability interact with those datasets, by exploring features, wrangling data, or creating an experiment.
---
# Datasets {: #datasets }
The **Datasets** tab lists all datasets currently linked to ... | wb-data-tab |
---
title: Data preparation
description: How to add, profile, and wrangle data in Workbench.
---
# Data preparation {: #data-preparation }
{% include 'includes/data-description.md' %}
DataRobot’s wrangling capabilities give you the ability to prepare data and engineer features with a no-code interface to see transfo... | index |
---
title: Notebook reference
description: Answers questions and provides tips for working with DataRobot Notebooks in DataRobot's Workbench.
section_name: Notebooks
---
{% include 'includes/notebooks/notebook-ref.md' %}
| wb-notebook-ref |
---
title: DataRobot Notebooks
description: Read preliminary documentation for DataRobot's notebook features currently in the DataRobot public preview pipeline.
section_name: Notebooks
---
# DataRobot Notebooks {: #datarobot-notebooks }
{% include 'includes/notebooks/nb-index-main.md' %}
## Browser compatibility {: ... | index |
---
title: Workbench overview
description: Understand the components of the DataRobot Workbench interface, including the architecture, some sample workflows, and directory landing page.
---
# Workbench overview {: #workbench-overview }
{% include 'includes/wb-overview.md' %}
| wb-overview |
---
title: Capability matrix
description: An evolving comparison of capabilities available in DataRobot Classic and Workbench.
---
# Capability matrix {: #capability-matrix }
{% include 'includes/wb-capability-matrix.md' %}
| wb-capability-matrix |
---
title: Get started
description: Understand the components of the new DataRobot user interface.
---
# Get started in Workbench {: #get-started-in-workbench }
Workbench is an intuitive, guided, machine learning workflow, providing a way for users to experiment and iterate. Move from raw data to prepared, partition... | index |
---
title: Glossary
description: Familiarize yourself with the terms specific to Workbench, DataRobot's collaborative, intuitive interface.
---
# Glossary {: #glossary }
The Workbench glossary provides brief definitions of terms relevant to the DataRobot's collaborative, intuitive interface. See the [full glossary](... | wb-glossary |
---
title: Create experiments
description: Describes how to create and manage experiments in the DataRobot Workbench interface.
---
# Create experiments {: #create-experiments }
Experiments are the individual "projects" within a [Use Case](wb-build-usecase). They allow you to vary data, targets, and modeling setting... | wb-experiment-create |
---
title: Experiment reference
description: Answers questions and provides tips for working with Use Cases in DataRobot's Workbench.
---
# Experiment reference {: #experiment-reference }
??? faq "What types of experiments are supported in Workbench?"
Currently Workbench supports binary classification and regressio... | wb-experiment-ref |
---
title: Experiments
description: Create experiments and iterate quickly to evaluate and select the best predictive models.
---
# Experiments {: #experiments }
The following sections help to understand Workbench experiments:
Topic | Describes...
---------|-----------------
[Create experiments](wb-experiment-cr... | index |
---
title: Evaluate experiments
description: Describes how to filter the Leaderboard and use visualization tools to evaluate models in the DataRobot Workbench interface.
---
# Evaluate experiments {: #evaluate-experiments }
Once you start modeling, Workbench begins to construct your model Leaderboard, a list of mode... | wb-experiment-evaluate |
---
title: Add models to experiments
description: Describes how to retrain Leaderboard models or add new models from the blueprint repository.
---
# Add models to experiments {: #add-models-to-experiments }
There are two methods for adding new models to your experiment:
- [Retrain](#train-on-new-settings) existing ... | wb-experiment-add |
---
title: No-Code AI App reference
description: Reference material for No-Code AI Apps in Workbench, including considerations and frequently asked questions (FAQs).
---
# No-Code AI App reference {: #no-code-ai-app-reference }
## FAQ {: #faq }
??? faq "How do I see which model an app was built from?"
In your Us... | wb-app-ref |
---
title: No-Code AI Apps
description: Create and configure AI-powered applications in Workbench using a no-code interface to enable core DataRobot services.
---
# No-Code AI Apps {: #no-code-ai-apps }
{% include 'includes/no-code-app-intro.md' %}
See the associated [considerations](app-builder/index#consideration... | index |
---
title: Make predictions
description: After you create an experiment and train models, you can upload scoring data, make predictions, and download the results.
---
# Make predictions {: #make-predictions }
After you create an [experiment](wb-experiment/index) and train models, you can make predictions to validate... | wb-predict |
---
title: Predictions reference
description: Answers questions and provides tips for working with Predictions in DataRobot's Workbench.
---
# Predictions reference {: #predictions-reference }
??? faq "What types of experiments are supported for Workbench predictions?"
Currently, Workbench supports predictions f... | wb-predict-ref |
---
title: Predictions
description: How to make predictions for a Workbench model.
---
# Predictions {: #predictions }
The following sections help to understand Workbench predictions:
Topic | Describes...
---------|-----------------
[Make predictions](wb-predict) | After you create an experiment and train mod... | index |
---
title: Use Cases
description: Describes creating a Use Case in DataRobot's Workbench and navigating the directory and assets.
---
# Use Cases {: #use-cases }
This section covers the following topics:
Topic | Describes...
---------|-----------------
[Use Cases](wb-build-usecase) | Create, share, and manage ... | index |
---
title: Use Case reference
description: Answers questions and provides tips for working with Use Cases in DataRobot's Workbench.
---
# Use Case reference {: #use-case-reference }
??? faq "How do you move between Workbench and DataRobot Classic?"
You can easily switch back and forth between Workbench and DataRobo... | wb-usecase-ref |
---
title: Use Cases
description: Learn how to build a Use Case in DataRobot's Workbench and investigate its assets.
---
# Use Cases {: #use-cases }
Use Cases are folder-like containers inside of DataRobot Workbench that allow you to group everything related to solving a specific business problem—datasets, mod... | wb-build-usecase |
---
title: Data registry
description: The Data Registry lists all static and snapshot datasets you currently have access to in the AI Catalog, including those uploaded from local files and data connections in Workbench.
---
# Data registry {: #data-registry }
When you open the **Add data** modal, by default, DataRob... | wb-data-registry |
---
title: Data connections
description: Connect to an external data source to seamlessly browse, preview, and profile data, as well as intiate scalable data preparation for machine learning with push-down.
---
# Data connections {: #data-connections }
In Workbench, you can easily configure and reuse secure connecti... | wb-connect |
---
title: Add data
description: In Workbench, you can add datasets from a local file, data connection, or the Data Registry.
---
# Add data {: #add-data }
From anywhere in a Use Case, you can add data by clicking **Add new > Add datasets**, opening the **Add data** modal. By adding data before setting up an experim... | index |
---
title: Local files
description: Add locally-stored datasets to your Use Case in Workbench.
---
# Upload local files {: #upload-local-files }
By uploading a local file via the Add data modal, you are both adding the dataset to your Use Case and [registering it in the Data Registry](wb-data-registry). This method o... | wb-local-file |
---
title: Smart downsampling
description: Use smart downsampling to reduce the size of your output dataset when publishing a wrangling recipe.
section_name: Workbench
maturity: public-preview
---
# Publish recipes with smart downsampling {: #publish-recipes-with-smart-downsampling }
!!! info "Availability informatio... | wb-downsample |
---
title: Public preview features
description: Read preliminary documentation for data-related features currently in the DataRobot public preview pipeline.
section_name: Workbench
maturity: public-preview
---
# Data preparation public preview features {: #data-preparation-public-preview-features }
{% include 'includ... | index |
---
title: Reference
description: Reference material for data workflows in Workbench, including considerations and frequently asked questions (FAQs).
---
# Data preparation reference {: #data-preparation-reference }
## FAQ {: #faq }
### Add data {: #add-data }
??? faq "What is the Data Registry and why does it sho... | index |
## Sample paths {: #sample-paths }
See the following paths for examples of adding and working with data in Workbench:
=== "via a data connection"
``` mermaid
flowchart TB
A(Add new > Add datasets) --> B(Connect to Snowflake);
B --> C(Preview a dataset);
C --> E(Does it need to be prepared?... | wb-data-paths |
---
title: Build a recipe
description: DataRobot leverages the compute environment and distributed architecture of your data source to quickly perform exploratory data analysis and apply transformations as you build your recipe.
---
# Build a recipe {: #build-a-recipe }
Building a recipe is the first step in prepari... | wb-add-operation |
---
title: Wrangle data
description: Apply transformations to a external source data, a Snowflake dataset for example, creating a recipe that can then be published to generate a new output dataset.
---
# Wrangle data {: #wrangle-data }
DataRobot's wrangling capabilities provide a seamless, scalable, and secure way to... | index |
---
title: Publish a recipe
description: Publish a recipe to push down transformations to your data source and generate an output dataset.
---
# Publish a recipe {: #publish-a-recipe }
Once the recipe is built and live sample looks ready for modeling, you can publish the recipe, pushing it down as a query to the dat... | wb-pub-recipe |
---
title: Add notebooks
description: Learn how to create new DataRobot notebooks, import existing notebooks, and export notebooks as .ipynb files.
section_name: Notebooks
maturity: public-preview
---
# Add notebooks {: #add-notebooks }
{% include 'includes/notebooks/create-nb.md' %}
| wb-create-nb |
---
title: Notebook versioning
description: Learn how to maintain versions of DataRobot Notebooks.
section_name: Notebooks
maturity: public-preview
---
# Notebook versioning {: #notebook-versioning }
{% include 'includes/notebooks/revise-nb.md' %}
| wb-revise-nb |
---
title: Notebook settings
description: Learn about the options available for DataRobot Notebook settings.
section_name: Notebooks
maturity: public-preview
---
# Notebook settings {: #notebook-settings }
{% include 'includes/notebooks/settings-nb.md' %}
| wb-settings-nb |
---
title: Manage notebooks
description: Learn how to create, configure, and manage DataRobot Notebooks.
section_name: Notebooks
maturity: public-preview
---
# Manage notebooks {: #manage-notebooks }
{% include 'includes/notebooks/manage-index.md' %}
| index |
---
title: Code intelligence
description: Describes the code intelligence capabilities available for code cells in DataRobot Notebooks.
section_name: Notebooks
---
# Code intelligence {: #code-intelligence}
{% include 'includes/notebooks/code-int.md' %}
| wb-code-int |
---
title: Cell actions
description: Describes the various actions available to control notebook cells.
section_name: Notebooks
---
# Cell actions {: #cell-actions }
{% include 'includes/notebooks/action-nb.md' %}
| wb-action-nb |
---
title: Notebook terminals
description: Describes the terminal integration available for DataRobot Notebooks.
section_name: Notebooks
---
# Notebook terminals {: #notebook-terminals }
{% include 'includes/notebooks/terminal-nb.md' %}
| wb-terminal-nb |
---
title: Notebook coding experience
description: Learn about the coding experience in DataRobot Notebooks.
section_name: Notebooks
---
# Notebook coding experience {: #notebook-coding-features }
{% include 'includes/notebooks/code-index.md' %}
| index |
---
title: Create and execute cells
description: Describes how to create and execute cells in DataRobot Notebooks.
section_name: Notebooks
---
# Create and execute cells {: #create-and-execute-cells}
{% include 'includes/notebooks/cell-nb.md' %}
| wb-cell-nb |
---
title: Azure OpenAI Service integration
description: Use Azure's OpenAI assistant to generate code in DataRobot Notebooks.
section_name: Notebooks
maturity: public-preview
---
# Azure OpenAI Service integration {: #azure-openai-service-integration }
{% include 'includes/notebooks/openai-nb.md' %}
| wb-openai-nb |
---
title: Environment management
description: Describes the environment management capabilities of the DataRobot Notebook platform.
section_name: Notebooks
---
# Environment management {: #environment-management }
{% include 'includes/notebooks/env-nb.md' %}
| wb-env-nb |
---
title: New app experience
description: DataRobot introduces an new, streamlined application experience in Workbench.
section_name: Workbench
maturity: public-preview
---
# New app experience {: #new-app-experience }
!!! info "Availability information"
The new application interface with model insights is off ... | wb-app-edit |
---
title: Public preview features
description: Read preliminary documentation for application-related features currently in the DataRobot public preview pipeline.
section_name: Workbench
maturity: public-preview
---
# Application public preview features {: #application-public-preview-features }
{% include 'includes/... | index |
---
title: Notebook versioning
description: Learn how to maintain versions of DataRobot Notebooks.
section_name: Notebooks
---
# Notebook versioning {: #notebook-versioning }
{% include 'includes/notebooks/revise-nb.md' %}
| dr-revise-nb |
---
title: Add notebooks
description: Learn how to create new DataRobot notebooks, import existing notebooks, and export notebooks as .ipynb files.
section_name: Notebooks
---
# Add notebooks {: #add-notebooks }
{% include 'includes/notebooks/create-nb.md' %}
| dr-create-nb |
---
title: Notebook settings
description: Learn about the options available for DataRobot Notebook settings.
section_name: Notebooks
---
# Notebook settings {: #notebook-settings }
{% include 'includes/notebooks/settings-nb.md' %}
| dr-settings-nb |
---
title: Manage notebooks
description: Learn how to create, configure, and manage DataRobot Notebooks.
section_name: Notebooks
---
# Manage notebooks {: #manage-notebooks }
{% include 'includes/notebooks/manage-index.md' %}
| index |
---
title: Environment management
description: Describes the environment management capabilities of the DataRobot Notebook platform.
section_name: Notebooks
---
# Environment management {: #environment-management }
{% include 'includes/notebooks/env-nb.md' %}
| dr-env-nb |
---
title: Create and execute cells
description: Describes how to create and execute cells in DataRobot Notebooks.
section_name: Notebooks
---
# Create and execute cells {: #create-and-execute-cells}
{% include 'includes/notebooks/cell-nb.md' %}
| dr-cell-nb |
---
title: Code intelligence
description: Describes the code intelligence capabilities available for code cells in DataRobot Notebooks.
section_name: Notebooks
---
# Code intelligence {: #code-intelligence}
{% include 'includes/notebooks/code-int.md' %}
| dr-code-int |
---
title: Cell actions
description: Describes the various actions available to control notebook cells.
section_name: Notebooks
---
# Cell actions {: #cell-actions }
{% include 'includes/notebooks/action-nb.md' %}
| dr-action-nb |
---
title: Notebook terminals
description: Describes the terminal integration available for DataRobot Notebooks.
section_name: Notebooks
---
# Notebook terminals {: #notebook-terminals }
{% include 'includes/notebooks/terminal-nb.md' %}
| dr-terminal-nb |
---
title: Azure OpenAI Service integration
description: Use Azure's OpenAI assistant to generate code in DataRobot Notebooks.
section_name: Notebooks
maturity: public-preview
---
# Azure OpenAI Service integration {: #openai-assistant }
{% include 'includes/notebooks/openai-nb.md' %}
| dr-openai-nb |
---
title: Notebook coding experience
description: Learn about the coding experience in DataRobot Notebooks.
section_name: Notebooks
---
# Notebook coding experience {: #notebook-coding-features }
{% include 'includes/notebooks/code-index.md' %}
| index |
---
title: Python client v3.0
description: Learn about the new features, enhancements, and changes in version 3.0 of DataRobot's Python client.
---
# Python client v3.0
Now generally available, DataRobot has released version 3.0 of the [Python client](https://pypi.org/project/datarobot/){ target=_blank }. This versi... | pythonv3 |
---
title: Modeling workflow overview
description: Learn how to use DataRobot's clients, both Python and R, to train and experiment with models.
---
# Modeling workflow overview {: #modeling-workflow-overview }
This code example outlines how to use DataRobot's clients, both Python and R, to train and experiment with... | modeling-workflow |
---
title: API user guide
description: Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows.
---
# API user guide {: #api-user-guide }
The API user guide includes overviews, Jupyter notebooks, and task-based tutorials that... | index |
---
title: Build a recommendation engine
description: Explore how to use historical user purchase data in order to create a recommendation model which will attempt to guess which products out of a basket of items the customer will be likely to purchase at a given point in time.
---
# Build a recommendation engine {: ... | rec-engine |
---
title: Create custom blueprints with composable ML
description: Customize models on the Leaderboard using the Blueprint Workshop.
---
# Create custom blueprints with composable ML {: #create-custom-blueprints-with-composable-ml}
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-ar... | custom-bp-nb |
---
title: Fine-tune models with Eureqa
description: Apply symbolic regression to your dataset in the form of the Eureqa algorithm.
---
# Fine-tune models with Eureqa {: #fine-tune-models-with-eureqa}
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](... | tune-eureqa |
---
title: Predict factory order quantities for new products
description: Build a model to improve decisions about initial order quantities using future product details and product sketches.
---
# Predict factory order quantities for new products {: #predict-factory-order-quantities-for-new-products }
[Access this A... | pred-products |
---
title: No-show appointment forecasting
description: How to build a model that identifies patients most likely to miss appointments, with correlating reasons.
---
# No-show appointment forecasting {: #no-show-appointment-forecasting }
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:materi... | no-show |
---
title: Demand forecasting with the what-if app
description: Discover the problem framing and data management steps required to successfully model for churn, using a B2C retail example and a B2B example based on a DataRobot’s churn model.
---
# Demand forecasting with the what-if app {: #demand-forecasting-with-th... | ml-what-if |
---
title: End-to-end ML workflow with Databricks
description: Build models in DataRobot with data acquired and prepared in a Spark-backed notebook environment provided by Databricks.
---
# End-to-end ML workflow with Databricks {: #end-to-end-ml-workflow-with-databricks }
[Access this AI accelerator on GitHub <span... | ml-databricks |
---
title: Perform multi-model analysis
description: Use Python functions to aggregate DataRobot model insights into visualizations.
---
# Perform multi-model analysis {: #perform-multi-model-analysis }
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>... | ml-analysis |
---
title: Integrate DataRobot and Snowpark by maximizing the data cloud
description: Use Python functions to aggregate DataRobot model insights into visualizations.
---
# Integrate DataRobot and Snowpark by maximizing the data cloud {: #integrate-datarobot-and-snowpark-by-maximizing-the-data-cloud }
[Access this AI... | snowpark-data |
---
title: Use self-joins with panel data to improve model accuracy
description: Explore how to implement self-joins in panel data analysis.
---
# Use self-joins with panel data to improve model accuracy {: #use-self-joins-with-panel-data-to-improve-model-accuracy }
[Access this AI accelerator on GitHub <span style=... | self-joins |
---
title: Create a trading volume profile curve with a time series model factory
description: Use a framework to build models that will allow you to predict how much of the next day trading volume will happen at each time interval.
---
# Create a trading volume profile curve with a time series model factory {: #crea... | ts-factory |
---
title: Predict lumber prices with Ready Signal and time series forecasts
description: Use Ready Signal to add external control data, such as census and weather data, to improve time series predictions.
---
# Predict lumber prices with Ready Signal and time series forecasts {: #predict-lumber-prices-with-ready-sig... | ready-signal |
---
title: Use Gramian angular fields to improve datasets
description: Generate advanced features used for high frequency data use cases.
---
# Use Gramian angular fields to improve datasets {: #use-gramian-angular-fields-to-improve-datasets }
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:m... | gramian |
---
title: End-to-end ML workflow with Google Cloud Platform and BigQuery
description: Use Google Collaboratory to source data from BigQuery, build and evaluate a model using DataRobot, and deploy predictions from that model back into BigQuery and GCP.
---
# End-to-end ML workflow with Google Cloud Platform and BigQu... | ml-gcp |
---
title: End-to-end modeling workflow with Azure
description: Use data stored in Azure to train a collection of models on DataRobot.
---
# End-to-end modeling workflow with Azure {: #end-to-end-modeling-workflow-with-azure}
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-rig... | ml-azure |
---
title: Use feature engineering and Visual AI with acoustic data
description: Generate image features in addition to aggregate numeric features for high frequency data sources.
---
# Use feature engineering and Visual AI with acoustic data {: #use-feature-engineering-and-visual-ai-with-acoustic-data }
[Access thi... | ml-viz |
---
title: Gather churn prediction insights with the Streamlit app
description: Use the Streamlit churn predictor app to present the drivers and predictions of your DataRobot model.
---
# Gather churn prediction insights with the Streamlit app {: #gather-churn-prediction-insights-with-the-streamlit-app }
[Access thi... | streamlit-app |
---
title: Prepare and leverage image data with Databricks
description: Import image files using Spark and prepare them into a data frame suitable for ingest into DataRobot.
---
# Prepare and leverage image data with Databricks {: #prepare-and-leverage-image-data-with-databricks }
[Access this AI accelerator on GitH... | image-databricks |
---
title: End-to-end workflow with SAP Hana
description: Learn how to programmatically build a model with DataRobot using SAP Hana as the data source.
---
# End-to-end workflow with SAP Hana {: #end-to-end-workflow-with-sap-hana }
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arr... | ml-sap |
---
title: Zero-shot text classification for error analysis
description: Use zero-shot text classification with large language models (LLMs), focusing on its application in error analysis of supervised text classification models.
---
# Zero-shot text classification for error analysis {: #zero-shot-text-classification... | zero-shot |
---
title: Tackle churn before modeling
description: Discover the problem framing and data management steps required to successfully model for churn, using a B2C retail example and a B2B example based on a DataRobot’s churn model.
---
# Tackle churn before modeling {: #tackle-churn-before-modeling }
[Access this AI ... | ml-churn |
---
title: Tune blueprints for preprocessing and model hyperparameters
description: Learn how to access, understand, and tune blueprints for both preprocessing and model hyperparameters.
---
# Tune blueprints for preprocessing and model hyperparameters {: #tune-blueprints-for-preprocessing-and-model-hyperparameters}
... | opt-grid |
---
title: Demand forecasting and retraining workflow
description: Implement retraining policies with DataRobot MLOps demand forecast deployments.
---
# Demand forecasting and retraining workflow {: #demand-forecasting-and-retraining-workflow }
[Access this AI accelerator on GitHub <span style="vertical-align: sub">... | df-retrain |
---
title: Customize lift charts
description: Leverage popular Python packages with DataRobot's Python client to recreate and augment DataRobot's lift chart visualization.
---
# Customize lift charts {: #customize-lift-charts }
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-r... | custom-lift-chart |
---
title: Cold start demand forecasting workflow
description: This accelerator provides a framework to compare several approaches for cold start modeling on series with limited or no history.
---
# Cold start demand forecasting workflow {: #cold-start-demand-forecasting-workflow}
[Access this AI accelerator on GitHu... | cold-start |
---
title: Netlift modeling workflow
description: Leverage machine learning to find patterns around the types of people for whom marketing campaigns are most effective.
---
# Netlift modeling workflow {: #netlift-modeling-workflow}
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arro... | ml-uplift |
---
title: Monitor AWS Sagemaker models with MLOps
description: Train and host a SageMaker model that can be monitored in the DataRobot platform.
---
# Monitor AWS Sagemaker models with MLOps {: #monitor-aws-sagemaker-models-with-mlops }
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:materi... | aws-mlops |
---
title: Deploy a model in AWS SageMaker
description: Learn how to programmatically build a model with DataRobot and export and host the model in AWS SageMaker
---
# Deploy a model in AWS SageMaker {: #deploy-a-model-in-aws-sagemaker }
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:materi... | deploy-sagemaker |
---
title: Track ML experiments with MLFlow
description: Automate machine learning experimentation using DataRobot, MLFlow, and Papermill.
---
# Track ML experiments with MLFlow {: #track-ml-experiments-with-mlflow }
[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle... | mlflow |
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