markdown stringlengths 44 160k | filename stringlengths 3 39 |
|---|---|
---
title: Platform home
description: Access the full DataRobot UI documentation, including feature descriptions for the UI and API, data preparation, tutorials, and a glossary.
---
# DataRobot UI documentation
The **UI docs** tab describes workflow and reference material for the UI version of the DataRobot AI Platf... | index |
---
title: ELI5
description: Explain it Like I'm 5 provides a list with brief, easily digestible answers. Answers links to more complete documentation.
---
# ELI5 {: #eli5 }
Explain it like I'm 5 (ELI5) contains complex DataRobot and data science concepts, broken down into brief, digestible answers. Many topics inclu... | eli5 |
---
title: Learn more
description: Get started in DataRobot with descriptions of common terms and concepts, as well as how-tos.
---
# Learn more {: #learn-more }
This page provides access to learning resources that help you get started in DataRobot, including simplified explanations of concepts, how-tos and end-to-en... | index |
---
title: MLOps
description: DataRobot machine learning operations (MLOps) provides a central hub for you to deploy, monitor, manage, and govern your models in production.
---
# MLOps {: #mlops }
DataRobot MLOps provides a central hub to deploy, monitor, manage, and govern all your models in production. You can dep... | index |
---
title: MLOps FAQ
dataset_name: N/A
description: Provides a list, with brief answers, of frequently asked MLOps deployment and monitoring questions. Answers link to complete documentation.
domain: mlops
expiration_date: 10-10-2024
owner: nick.aylward@datarobot.com
url: docs.datarobot.com/docs/mlops/mlops-faq.html
-... | mlops-faq |
---
title: Platform
description: This section includes information and links for managing user settings; authentication and SSO; the administrator's guide; sharing and permissions; user documentation for companion tools; and more.
---
# Platform {: #platform }
The platform section provides materials for users and adm... | index |
With the **Comments** link, you can add comments to—even host a discussion around—any item in the catalog that you have access to. Comment functionality is available in the **AI Catalog** (illustrated below), and also as a model tab from the Leaderboard and in use case tracking. With comments you can:
* Ta... | comm-add |
??? note "Dataset requirements for time series batch predictions"
To ensure DataRobot can process your time series data, configure the dataset to meet the following requirements:
* Sort prediction rows by their timestamps, with the earliest row first.
* For multiseries, sort prediction rows by series ID an... | batch-pred-ts-scoring-data-requirements |
!!! note "DataRobot fully supports the latest version of Google Chrome"
Other browsers such as Edge, Firefox, and Safari are not fully supported. As a result, certain features may not work as expected. DataRobot recommends using Chrome for the best experience. Ad block browser extensions may cause display or perfo... | browser-compatibility |
The **Clustering** tab sets the number of clusters that DataRobot will find during Autopilot. The default number of clusters is based on number of series in the dataset.
To set the number, add or remove values from the entry box and select the value from the dropdown:
Note that wh... | ts-cluster-adv-opt-include |
There are several options available in the **Actions** menu, which can be accessed for each model package in the **Model Packages** tab of the **Model Registry**:
The available options depend on a variety of criteria, including user permissions and the data available to your model package... | manage-model-packages |
??? faq "How does DataRobot track drift?"
For data drift, DataRobot tracks:
* **Target drift**: DataRobot stores statistics about predictions to monitor how the distribution and values of the target change over time. As a baseline for comparing target distributions, DataRobot uses the distribution of predictio... | how-dr-tracks-drift-include |
## Deep dive: Imbalanced targets {: #deep-dive-imbalanced-targets }
In AML and Transaction Monitoring, the SAR rate is usually very low (1%–5%, depending on the detection scenarios); sometimes it could be even lower than 1% in extremely unproductive scenarios. In machine learning, such a problem is called _class imbal... | aml-4-include |
* Frozen thresholds are not supported.
* Blenders that contain monotonic models do not display the MONO label on the Leaderboard for OTV projects.
* When previewing predictions over time, the interval only displays for models that haven’t been retrained (for example, it won’t show up for models with the **Recommended... | dt-consider |
| | Element | Description |
|---|---|---|
|  | Filter by predicted or actual | Narrows the display based on the predicted and actual class values. See [Filters](#filters) for details.|
|  | Show color overlay | Sets whether to display the activation map in either black and ... | activation-map-include |
Consider the following when working with segmented modeling deployments:
* Time series segmented modeling deployments do not support data drift monitoring.
* Automatic retraining for segmented deployments that use clustering models is disabled; retraining must be done manually.
* Retraining can be triggered by ac... | deploy-combined-model-include |
The **Histogram** chart is the default display for numeric features. It "buckets" numeric feature values into equal-sized ranges to show frequency distribution of the variable—the target observation (left Y-axis) plotted against the frequency of the value (X-axis). The height of each bar represents the number of ... | histogram-include |
## Business problem {: #business-problem }
A key pillar of any AML compliance program is to monitor transactions for suspicious activity. The scope of transactions is broad, including deposits, withdrawals, fund transfers, purchases, merchant credits, and payments. Typically, monitoring starts with a rules-based syste... | aml-1-include |
Data integrity and quality are cornerstones for creating highly accurate predictive models. These sections describe the tools and visualizations DataRobot provides to ensure that your project doesn't suffer the "garbage in, garbage out" outcome.
| data-description |
### Business problem {: #business-problem }
A "readmission" event is when a patient is readmitted into the hospital within 30 days of being discharged. Readmissions are not only a reflection of uncoordinated healthcare systems that fail to sufficiently understand patients and their conditions, but they are also a tre... | hospital-readmit-include |
## DRUM on Windows with WSL2 {: #drum-on-windows-with-wsl2 }
DRUM can be run on Windows 10 or 11 with WSL2 (Windows Subsystem for Linux), a native extension that is supported by the latest versions of Windows and allows you to easily install and run Linux OS on a Windows machine. With WSL, you can develop custom tasks... | drum-for-windows |
| | Element | Description |
|--|---------|-------------|
|  | Include input features | Writes input features to the prediction results file alongside predictions. To add specific features, enable the **Include input features** toggle, select **Specific features**, and ty... | prediction-options-include |
Log in to GitHub before accessing these GitHub resources.
| github-sign-in-plural |
## About final models {: #about-final-models }
The original ("final") model is trained without holdout data and therefore does not have the most recent data. Instead, it represents the first backtest. This is so that predictions match the insights, coefficients, and other data displayed in the tabs that help evaluate ... | date-time-include-5 |
## Troubleshooting {: #troubleshooting }
Problem | Solution | Instructions
---------- | ----------- | ---------------
When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all whitelisted IPs for DataRobot. | See [Source IP addresses for w... | data-conn-trouble |
DataRobot detects the date and/or time format (<a target="_blank" href="https://docs.python.org/2/library/datetime#strftime-and-strptime-behavior">standard GLIBC strings</a>) for the selected feature. Verify that it is correct. If the format displayed does not accurately represent the date column(s) of your dataset, mo... | date-time-include-1 |
| | Element | Description |
|---|---|---|
|  | Selected word | Displays details about the selected word. (The term *word* here equates to an [*n-gram*](glossary/index#ngram), which can be a sequence of words.) <br><br>Mouse over a word to select it. Words that appear more frequently display in a ... | word-cloud-include |
??? info "Category Cloud availability"
The **Category Cloud** insight is available on the **Models > Insights** tab and on the **Data** tab. On the **Insights** page, you can compare word clouds for a project's categorically-based models. From the **Data** page you can more easily compare clouds across features. No... | category-cloud-include |
## Predict and deploy {: #predict-and-deploy }
Once you identify the model that best learns patterns in your data to predict SARs, you can deploy it into your desired decision environment. *Decision environments* are the ways in which the predictions generated by the model will be consumed by the appropriate organizat... | aml-3-include |
Log in to GitHub before clicking this link.
| github-sign-in |
??? note "Time series blueprints with Scoring Code support"
<span id="ts-sc-blueprint-support">The following blueprints typically support Scoring Code:</span>
* AUTOARIMA with Fixed Error Terms
* ElasticNet Regressor (L2 / Gamma Deviance) using Linearly Decaying Weights with Forecast Distance Modeling
... | scoring-code-consider-ts |
No-Code AI Apps allow you to build and configure AI-powered applications using a no-code interface to enable core DataRobot services without having to build models and evaluate their performance in DataRobot. Applications are easily shared and do not require users to own full DataRobot licenses in order to use them. Ap... | no-code-app-intro |
The **Over time** chart helps you identify trends and potential gaps in your data by displaying, for both the original modeling data and the derived data, how a feature changes over the primary date/time feature. It is available for all time-aware projects (OTV, single series, and multiseries). For time series, it is a... | date-time-include-4 |
### Business problem {: #business-problem }
Because, on average, it takes roughly 20 days to process an auto insurance claim (which often frustrates policyholders), insurance companies look for ways to increase the efficiency of their claims workflows. Increasing the number of claim handlers is expensive, so companies... | fraud-claims-include |
The execution environment limit allows you to control how many custom model environments a user can add to the [Custom Model Workshop](custom-model-workshop/index). In addition, the execution environment _version_ limit allows you to control how many versions a user can add to _each_ of those environments. These limits... | ex-env-limits |
## Feature considerations {: #feature-considerations }
Consider the following when working with Scoring Code:
* Using Scoring Code in production requires additional development efforts to implement model management and model monitoring, which the DataRobot API provides out of the box.
* Exportable Java Scoring Code ... | scoring-code-consider |
!!! note
Some [DataRobot University](https://university.datarobot.com){ target=_blank } courses require subscriptions.
| dru-subscription |
The [metrics values](#metrics-explained) on the ROC curve display might not always match those shown on the Leaderboard. For ROC curve metrics, DataRobot keeps up to 120 of the calculated thresholds that best represent the distribution. Because of this, minute details might be lost. For example, if you select **Maximiz... | max-metrics-roc |
## Time-aware models on the Leaderboard {: #time-aware-models-on-the-leaderboard }
Once you click **Start**, DataRobot begins the model-building process and returns results to the Leaderboard.
!!! note
Model parameter selection has not been customized for date/time-partitioned projects. Though automatic parameter... | date-time-include-3 |
The following sections describe the components to making predictions in DataRobot:
* [Using the Prediction API](../../../api/reference/predapi/index)
* [Using Scoring Code](../../../predictions/scoring-code/index)
* [Using batch Scoring](../../../predictions/batch/index)
* [Using the UI to make predictions](../../../p... | pred-tab |
The sample dataset contains patient data.
The goal is to predict the likelihood of patient readmission to the hospital. The target feature is `readmitted`.
| tu-dataset-patient-data |
DataRobot's Workbench interface streamlines the modeling process, minimizing time-to-value while still leveraging cutting-edge ML techniques. It is designed to match the data scientist's iterative workflows with a cleaner interface for easier project creation and model review, smooth navigation, and all key insights in... | wb-overview |
!!! note
When ingesting data into a Pipeline Workspace via the AI Catalog Import module, if DataRobot cannot intepret the column as a date, time, or timestamp, it is converted to a string column and the data type must be manually updated. Data type detection accepts the same [date and time formats](file-types#date-... | pl-temp-data |
The following table provides an evolving comparison of capabilities available in DataRobot Classic and Workbench.
Feature | DataRobot Classic | Workbench
------- |------------------ | ---------
_**General platform features**_ | :~~: | :~~:
Sharing | Data, projects | Data, Use Cases
Business-wide solution | No, single ... | wb-capability-matrix |
---
title: Solution accelerators
description: This section provides access to the catalog of use case-based solution accelerators, segmented by industry.
---
# Solution accelerators {: #solution-accelerators }
Solution accelerators provide a packaged solution, based on best practices and patterns, to address the most... | index |
## Understanding backtests {: #understanding-backtests }
Backtesting is conceptually the same as cross-validation in that it provides the ability to test a predictive model using existing historical data. That is, you can evaluate how the model would have performed historically to estimate how the model will perform i... | date-time-include-6 |
| | Element | Description |
|---|---|---|
|  | Filter | Allows you to select a specific [class](vai-ref#image-embeddings)*) to display. All classes display by default.|
|  | Image display | Displays projections of images in two dimensions to help you visualize similarities b... | image-embeddings-include |
## Business problem {: #business-problem }
After the 2008 financial crisis, the IASB (International Accounting Standard Board) and FASB (Financial Accounting Standards Board) reviewed accounting standards. As a result, they updated policies to require estimated Expected Credit Loss (ECL) to maintain enough regulatory ... | loan-defaults-include |
!!! note
If you add a [secondary dataset](fd-overview) with images to a primary tabular dataset, the augmentation options described above are not available. Instead, if you have access to Composable ML, you can [modify each needed blueprint](cml-blueprint-edit) by adding an image augmentation vertex directly after the... | image-augmentation-include |
Because of the complexity of many machine learning techniques, models can sometimes be difficult to interpret directly. **Feature Fit** and **Feature Effects** provide similar model detail insights on a per-feature basis. Feature Fit, under the [Evaluate](evaluate/index) tab, ranks features based on the importance scor... | ff-fe |
## Build time-aware models {: #build-time-aware-models }
Once you click **Start**, DataRobot begins the model-building process and returns results to the Leaderboard. Because time series modeling uses date/time partitioning, you can run backtests, change window sampling, change training periods, and more from the Lead... | date-time-include-2 |
??? faq "Drift metric support"
<span id="drift-metric-support">While the DataRobot UI only supports the Population Stability Index (PSI) metric, the API supports Kullback-Leibler Divergence, Hellinger Distance, Kolmogorov-Smirnov, Histogram Intersection, Wasserstein Distance, and Jensen–Shannon Divergence. In addit... | drift-metrics-support |
Compare the characteristics and capabilities of the two types of custom models below:
Model type | Characteristics | Capabilities
------------------|-----------------|--------------
Structured | <ul><li>Uses a target type known to DataRobot (e.g., regression, binary classification, multiclass, and anomal... | structured-vs-unstructured-cus-models |
!!! note
Specified pairwise interactions are not guaranteed to appear in a model's output. Only the interactions that add signal to a model according to the algorithm will be featured in the output. For example, if you specify an interaction group of features A, B, and C, then AxB, BxC, and AxC are the interactions... | pairwise-warning |
## Modeling and insights {: #modeling-and-insights }
DataRobot automates many parts of the modeling pipeline, including processing and partitioning the dataset, as described [here](model-data). This document starts with the visualizations available once modeling has started.
### Exploratory Data Analysis (EDA) {: #ex... | aml-2-include |
To create a deployment from the Leaderboard:
1. From the Leaderboard, select the model to use for generating predictions and click **Predict > Deploy**. The **Deploy model** page lets you create a new deployment for the selected model. In this example, the model is both recommended for deployment and prepared for depl... | deploy-leaderboard |
DataRobot offers portable prediction methods, allowing you to execute prediction jobs outside of the DataRobot application. The portable prediction methods are detailed below:
Method | Description
------ | ------------
[Scoring Code](scoring-code/index) | You can export Scoring Code from DataRobot in Java or Python to... | port-pred-options |
The **Time Series** tab sets a variety of features that can be set to customize time series projects.
Using the advanced options settings can impact DataRobot's feature engineering and how it models data. There are a few reasons to work with these options, although for most users, the defaults that DataRobot selects p... | ts-adv-opt-include |
If you expect to be able to increase your worker count but cannot, the reasons may be:
* You have hit your [worker limit](#worker-limit).
* Your workers are part of a [shared pool](#pooled-workers).
* Your workers are [in use by another project](#workers-in-use).
#### Worker limit {: #worker-limit }
[Modeling worker... | worker-queue-tbsht-include |
## DRUM on Mac {: #drum-on-mac }
The following instructions describe installing DRUM with `conda` (although you can use other tools if you prefer) and then using DRUM to test a task locally. Before you begin, DRUM requires:
* An installation of [`conda`](https://docs.conda.io/en/latest/miniconda.html){ target=_blank... | drum-for-mac |
| Host: https://app.datarobot.com | Host: https://app.eu.datarobot.com |
|---------------------------------|------------------------------------|
| 100.26.66.209 | 18.200.151.211 |
| 54.204.171.181 | 18.200.151.56 |
| 54.145.89.18 | 18.200.151.43 |
| 54.147.212.247 | 54.78.199.18 |
| 18.235.157.68 | 54.78.... | whitelist-ip |
# DRUM CLI tool {: #drum-cli-tool }
DataRobot user model (DRUM) is a CLI tool that allows you to work with Python, R, and Java custom models and to quickly test [custom tasks](cml-custom-tasks), [custom models](custom-models/index), and [custom environments](custom-environments) locally before uploading into DataRobot... | drum-tool |
This visualization supports sliced insights. Slices allow you to define a user-configured subpopulation of a model's data based on feature values, which helps to better understand how the model performs on different segments of data. See the full [documentation](sliced-insights) for more information. | slices-viz-include |
!!! note "Time of Prediction"
The <span id="time-of-prediction">Time of Prediction</span> value differs between the [Data Drift](data-drift) and [Accuracy](deploy-accuracy) tabs and the [Service Health](service-health) tab:
* On the Service Health tab, the "time of prediction request" is _always_ the tim... | service-health-prediction-time |
| Prediction method | Details | File size limit |
|-------------------|---------|-----------------|
| Leaderboard predictions | To make predictions on a non-deployed model using the UI, expand the model on the Leaderboard and select [**Predict > Make Predictions**](predict). Upload predictions from a local file, URL, d... | pred-limits-include |
This section provides preliminary documentation for features currently in the public preview pipeline. If not enabled for your organization, the feature is not visible.
Although these features have been tested within the engineering and quality environments, they should not be used in production at this time. Note tha... | pub-preview-notice-include |
To configure the **Time series options**, under **Time series prediction method**, select [**Forecast point** or **Forecast range**](ts-predictions#forecast-settings).
=== "Forecast point"
Select **Forecast point** to choose the specific date from which you want to begin making predictions, and then select a **Fo... | batch-pred-jobs-ts-options-include |
## Business problem {: #business-problem }
Claim payments and claim adjustment are typically an insurance company’s largest expenses. For long-tail lines of business, such as workers’ compensation (which covers medical expenses and lost wages for injured workers), the true cost of a claim may not be known for many yea... | triage-insurance-claims-include |
## DRUM on Ubuntu {: #drum-on-ubuntu }
The following describes the DRUM installation workflow. Consider the language prerequisites before proceeding.
| Language | Prerequisites | Installation command |
|----------|----------------------|------------------------------|
| Python | Python 3 recommended ... | drum-for-ubuntu |
To configure the **Time series options**, under **Time series prediction method**, select [**Forecast point** or **Forecast range**](ts-predictions#forecast-settings).
=== "Forecast point"
Select **Forecast point** to choose the specific date from which you want to begin making predictions, and then select a **Fo... | batch-pred-ts-options-include |
---
title: Managed AI Platform releases
description: Read release announcements for DataRobot's generally available and public preview features released in June, 2023.
---
# Managed AI Platform releases {: #managed-ai-platform-releases }
This page provides announcements of newly released features available in DataRo... | index |
---
title: Modeling
description: Learn about the modeling process. Covers setting modeling parameters before building, modeling workflow, managing models and projects, and exporting data.
---
# Modeling {: #modeling }
The sections described below provide information to help you easily navigate the ML modeling proces... | index |
---
title: Modeling FAQ
dataset_name: N/A
Description: Provides a list of frequently asked questions, and brief answers about general modeling, building models, and model insights in DataRobot. Answers link to more complete documentation.
domain: core modeling
expiration_date: 10-10-2024
owner: jen@datarobot.com
url: d... | general-modeling-faq |
---
title: Workbench
description: Workbench provides an organizational hierarchy that, from a Use Case as the top-level asset, supports experimentation and sharing.
---
# Workbench {: #workbench }
The components and workflow that comprise DataRobot's Workbench interface are summarized in the following sections:
Top... | 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' %}
| dr-notebook-ref |
---
title: DataRobot Notebooks
description: Read documentation for DataRobot's notebook platform.
section_name: Notebooks
---
# Notebooks {: #notebooks }
{% include 'includes/notebooks/nb-index-main.md' %}
## Browser compatibility {: #browser-compatibility }
{% include 'includes/browser-compatibility.md' %}
| index |
---
title: DataRobot API resources
description: Use the REST, Python, and R APIs as a programmatic alternative to the UI for creating and managing DataRobot projects.
---
# DataRobot API resources {: #api-documentation-home }
DataRobot supports REST, Python, and R APIs as a programmatic alternative to the UI for crea... | index |
---
title: Dataset requirements
description: Detailed dataset requirements for file size and format, rows, columns, encodings and characters sets, column length and name conversion, and more.
---
# Dataset requirements {: #dataset-requirements }
This section provides information on dataset requirements:
* [General ... | file-types |
---
title: Data
description: How to manage data for machine learning, including importing and transforming data, and connecting to data sources.
---
# Data {: #data }
{% include 'includes/data-description.md' %}
See the associated [considerations](#feature-considerations) for important additional information. See a... | index |
---
title: Data FAQ
dataset_name: N/A
description: Provides a list, with brief answers, of frequently asked data preparation and management questions. Answers links to more complete documentation.
domain: platform
expiration_date: 10-10-2025
owner: josh.klaben.finegold@datarobot.com
url: docs.datarobot.com/docs/tutoria... | data-faq |
---
title: Get started
description: Get started with DataRobot's value-driven AI. Analyze data, create and deploy models, and leverage code-first accelerators and notebooks.
---
# Get started {: #get-started }
Get started with DataRobot's value-driven AI. Analyze data, create and deploy models, and leverage code-fir... | index |
---
title: Predictions reference
description: Learn the file size limits for different methods of making predictions. Prediction file size limits depend on whether the model is deployed or not and whether you use the UI or an API.
---
# Prediction reference {: #prediction-reference }
DataRobot supports many methods o... | pred-file-limits |
---
title: Predictions
description: Learn the methods and DataRobot components for getting predictions (“scoring”) on new data from a model. To make predictions, you can use real-time predictions, batch predictions, or portable prediction methods.
---
# Predictions {: #predictions }
DataRobot offers several methods ... | index |
---
title: Predictions testing
description: To make predictions and assess model performance prior to deployment, you can make predictions on an external test dataset (i.e., external holdout) or on training data (i.e., validation and/or holdout).
---
# Predictions on test and training data {: #predictions-on-test-and-... | pred-test |
---
title: Create applications
description: Create No-Code AI Apps to enable core DataRobot services while using a no-code interface.
---
# Create applications {: #create-applications}
You can create applications in DataRobot from the [**Applications**](#from-the-applications-tab) tab, a [model on the Leaderboard](#... | create-app |
---
title: Manage applications
description: View, share, and delete current No-Code AI Apps.
---
# Manage applications {: #manage-applications }
In addition to creating apps from the **Applications** tab, you can view all existing applications that you have created or have been shared with you.
 Alert Scoring
description: Build a model that uses historical data, including customer and transactional information, to identify which alerts resulted in a Suspicious Activity Report (SAR).
---
# Anti-Money Laundering (AML) Alert Scoring {: #anti-money-laundering-aml-alert-scori... | money-launder |
---
title: Business accelerators
description: A catalog of UI-based, end-to-end walkthroughs that address common industry-specific problems.
---
# Business accelerators {: #business-accelerators }
This section provides access to a catalog of UI-based, end-to-end walkthroughs, based on best practices and patterns, tha... | index |
---
title: Fraudulent claim detection
description: Improve the accuracy in predicting which insurance claims are fraudulent.
---
# Fraudulent claim detection {: #fraudulent-claim-detection }
This page outlines the use case to improve the accuracy in predicting which insurance claims are fraudulent. It is captured bel... | fraud-claims |
---
title: Business application briefs
description: A variety of quick summary applications with an accompanying No-Code AI App to provide an overview of possible uses.
---
# Business application briefs {: #business-application-briefs }
This section provides a variety of quick use case summaries, with an accompanying... | biz-app-briefs |
---
title: Purchase card fraud detection
description: Helps organizations that employ purchase cards for procurement monitor for fraud and misuse.
---
# Purchase card fraud detection {: #purchase-card-fraud-detection }
In this use case you will build a model that can review 100% of purchase card transactions and ide... | p-card-detect |
---
title: Late shipment predictions
description: Helps supply chain managers can evaluate root cause and then implement short-term and long-term adjustments that prevent shipping delays.
---
# Late shipment predictions {: #late-shipment-prediction }
With the inception of one-day and same-day delivery, customer stand... | late-ship |
---
title: Android integration
description: Learn how to use Java Scoring Code on Android with little or no modifications. Supported only for Android 8.0 (API 26) or later.
---
# Android integration {: #android-integration }
It is possible to use Java Scoring Code on Android with little or no modifications.
!!! not... | android |
---
title: Apache Spark API for Scoring Code
description: Learn how to use the Spark API for Scoring Code, a library that integrates Scoring Code JARs into Spark clusters.
---
# Apache Spark API for Scoring Code {: #apache-spark-api-for-scoring-code }
The Spark API for Scoring Code library integrates DataRobot Scori... | sc-apache-spark |
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