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---
title: Alteryx
description: Provides documentation for a tool that allows you to create projects and make predictions without leaving the Alteryx interface.
---
# Tools for Alteryx {: #alteryx }
DataRobot Tools allow you to create projects and make predictions without leaving the Alteryx interface.
1. Download ... | alteryx |
---
title: How-tos
description: Step-by-step instructions to perform tasks within the DataRobot application as well as partners, cloud providers, and 3rd party vendors.
---
# How-tos {: #how-tos }
These sections provide step-by-step instructions to perform tasks within the DataRobot application as well as partners, ... | index |
---
title: Tableau Extension URL
description: Provides documentation for changing the Tableau TREX configuration to work with DataRobot deployments.
---
# Tableau Extension URL {: #tableau-extension-url }
The DataRobot extensions for Tableau, downloadable from the <a target="_blank" href="https://extensiongallery.ta... | tableau |
---
title: Apache Airflow
description: How to use the DataRobot Provider for Apache Airflow to implement a basic DAG orchestrating an end-to-end DataRobot AI pipeline.
---
# DataRobot provider for Apache Airflow
The combined capabilities of [DataRobot MLOps](mlops/index) and [Apache Airflow](https://airflow.apache.... | apache-airflow |
---
title: PCA and K-Means clustering
description: The impact of principal component analysis (PCA) on KMeans in DataRobot modeling
---
# PCA and K-Means clustering {: #pca-and-k-means-clustering }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**What is the impact of principal component analysis (PCA) on... | rr-pca-kmeans |
---
title: Prediction Explanations on small data
description: How to generate Prediction Explanations in DataRobot when working with small datasets.
---
# Prediction Explanations on small data {: #prediction-explanations-on-small-data }
!!! warning
The described workaround is intended for users who are very familiar... | rr-predex-small-data |
---
title: Dynamic time warping (DTW)
description: Does dynamic time warping attempt to align the endpoint of series that may not be entirely overlapping?
---
# Dynamic time warping (DTW) {: #dynamic-time-warping-dtw }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**It is my understanding that dynamic ti... | rr-dynamic-time |
---
title: Single- vs. multi-tenant SaaS
description: DataRobot supports both single-tenant and multi-tenant SaaS and here's what it means.
---
# Single- vs. multi-tenant SaaS {: #single-vs-multi-tenant-saas }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**What do we mean by Single-tenant and multi-tena... | rr-singlemulti-tenant |
---
title: Normalizing for monotonicity
description: With DataRobot's monotonicity, to normalize or not to normalize, that is the question.
---
# Normalizing for monotonicity {: #normalizing-for-monotonicity }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**To normalize or not to normalize, that is the q... | rr-norm-for-mono |
---
title: Ordered categoricals
description: With DataRobot's monotonicity, to normalize or not to normalize, that is the question.
---
# Ordered categoricals {: #ordered-categoricals }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
Does DataRobot recognize ordered categoricals, like grade in the infamous... | rr-ordered-cat |
---
title: Model integrity and security
description: What measures does the platform support to assure the integrity and security of AI models?
---
# Model integrity and security {: #model-integrity-and-security }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**What measures does the platform support t... | rr-model-integrity |
---
title: Word Cloud repeats
description: Why would a term would show up multiple times in a word cloud?
---
# Word Cloud repeats {: #word-cloud-repeats }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**Why would a term would show up multiple times in a word cloud? **
And for those occurrences, why wou... | rr-word-cloud |
---
title: Target transform
description: How does transforming your target (log, ^2) help ML models and when should you use each?
---
# Target transform {: #target-transform }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**How does transforming your target (`log(target)`, `target^2`, etc.) help ML mode... | rr-target-transform |
---
title: Defining redundant features
description: How does DataRobot define similarity in features and call them redundant?
---
# Defining redundant features {: #defining-redundant-features }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**What makes a feature redundant?**
The [docs](feature-impact#re... | rr-redundant-features |
---
title: Import for Keras or TF
description: Use DataRobot custom models to import custom inference models.
---
# Import for Keras or TF {: #import-for-keras-or-tf }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**Is there a way to import .tf or .keras models?**
<span style="color:red;font-size: 1rem"... | rr-import-keras |
---
title: ACE score and row order
description: Does DataRobot's ACE score, a univariate measure of correlation, depend on row order?
---
# ACE score and row order {: #ace-score-and-row-order }
!!! faq "What is ACE?"
ACE scores (Alternating Conditional Expectations) are a univariate measure of correlation between th... | rr-ace-score |
---
title: Neural networks and tabular data
description: A compendium of reasons why you don't need neural networks with tabular data.
---
# Neural networks and tabular data {: #neural-networks-and-tabular-data }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**Can someone share research on why we don't ... | rr-nns-tabular-data |
---
title: NPS in DataRobot
description: Using DataRobot to implement an NPS (net promoter scores) solution.
---
# NPS in DataRobot {: #nps-in-datarobot }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**Has anyone implemented an NPS solution in DataRobot?**
Hello NLP team. I was wondering if anyone has ... | rr-nps-scores |
---
title: Offset/exposure with Gamma distributions
description: With DataRobot's monotonicity, to normalize or not to normalize, that is the question.
---
# Offset/exposure with Gamma distributions {: #offset-exposure-with-gamma-distributions }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**How does Da... | rr-offset-exposure-gamma |
---
title: Intermittent target leakage
description: DataRobot's target leakage detection explained.
---
# Intermittent target leakage {: #intermittent-target-leakage }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**Why might target leakage show intermittently?**
Hi team! A student in the DataRobot for... | rr-target-leakage |
---
title: Robot-to-Robot
description: DataRobot employees ask and answer question questions related to the platform and data science.
---
# Robot-to-Robot {: #robot-to-robot }
This section pulls back the curtain to reveal what DataRobot employees talk about in Slack. No surprises here—data science is still top... | index |
---
title: N-grams and prediction confidence
description: Which DataRobot tools help understand n-gram predictions?
---
# N-grams and prediction confidence {: #n-grams-and-prediction-confidence }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**Customer question: how do we know which words/n-grams increas... | rr-n-gram-predictions |
---
title: Alternate use for a payoff matrix
description: An interesting way to use DataRobot's payoff matrix, consider justifying cost vs. identifying profit drivers.
---
# Alternate use for a payoff matrix {: #alternate-use-for-a-payoff-matrix }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**How abo... | rr-payoff-matrix |
---
title: GPU vs. CPU
description: DataRobot applies GPUs or CPUs depending on the task, as explained here.
---
# GPU vs. CPU {: #gpu-vs-cpu }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**How does CPU differ from GPU (in terms of training ML models)?**
<span style="color:red;font-size: 1rem"> `Ro... | rr-gpu-v-cpu |
---
title: Default language change in Japanese
description: DataRobot's natural language processing heuristics improvements.
---
# Default language change in Japanese {: #default-language-change-in-japanese }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**Why did the default language change when modelin... | rr-language-nlp |
---
title: Multiple DR Reduced Features lists
description: How to have multiple Reduced Features lists in one project.
---
# Multiple DR Reduced Features lists {: #multiple-reduced-feature-lists }
<span style="color:red;font-size: 1rem"> `Robot 1`</span>
**Can I have multiple DR Reduced Features lists for one projec... | rr-reduced-feature-lists |
---
title: Glossary home
description: The Glossary provides brief definitions of terms relevant to the DataRobot platform.
---
# Glossary
The DataRobot glossary provides brief definitions of terms relevant to the DataRobot platform. These terms span all phases of machine learning, from data to deployment.
[All](#){... | index |
---
title: Deploy and monitor Spark models with DataRobot MLOps
description: Deploy and monitor Spark models with DataRobot MLOps
---
# Deploy and monitor Spark models with DataRobot MLOps {: #deploy-and-monitor-spark-models-with-datarobot-mlops }
This page shows how to use DataRobot's Monitoring Agent (MLOps agent) ... | spark-deploy-and-monitor |
---
title: Deploy and monitor DataRobot models in Azure Kubernetes Service
description: Deploy and monitor DataRobot models in Azure Kubernetes Service
---
# Deploy and monitor DataRobot models in Azure Kubernetes Service {: #deploy-and-monitor-datarobot-models-in-azure-kubernetes-service }
!!! info "Availability in... | aks-deploy-and-monitor |
---
title: Run Batch Prediction jobs from Azure Blob Storage
description: Run Batch Prediction jobs from Azure Blob Storage
---
# Run Batch Prediction jobs from Azure Blob Storage
The DataRobot Batch Prediction API allows users to take in large datasets and score them against deployed models running on a Prediction ... | azure-blob-storage-batch-pred |
---
title: Azure
description: Integrate DataRobot with Azure cloud services.
---
# Azure {: #azure }
The sections within describe techniques for integrating Azure cloud services with DataRobot:
Topic | Describes...
----- | ------
[Run Batch Prediction jobs from Azure Blob Storage](azure-blob-storage-batch-pred) | R... | index |
---
title: Deploy and monitor ML.NET models with DataRobot MLOps
description: Deploy and monitor ML.NET models with DataRobot MLOps
---
# Deploy and monitor ML.NET models with DataRobot MLOps {: #deploy-and-monitor-ml-net-models-with-datarobot-mlops }
This page explores how models built with ML.NET can be deployed a... | mlnet-deploy-and-monitor |
---
title: Use Scoring Code with Azure ML
description: Import Scoring Code models to Azure ML to make prediction requests using Azure.
---
# Use Scoring Code with Azure ML {: #use-scoring-code-with-azure-ml }
You must complete the following before importing Scoring Code models to Azure ML:
* Install the <a target="... | sc-azureml |
---
title: Deploy and monitor models on GCP
description: Deploy and monitor DataRobot models on Google Cloud Platform (GCP).
---
# Deploy and monitor models on GCP {: #deploy-and-monitor-models-on-gcp }
!!! info "Availability information"
The MLOps model package export feature used in this procedure is off by de... | google-cloud-platform |
---
title: Deploy the MLOps agent on GKE
description: Deploy the MLOps agent on GKE to monitor DataRobot models.
---
# Deploy the MLOps agent on GKE {: #deploy-the-mlops-agent-on-gke }
The following steps describe how to deploy the MLOps agent on Google Kubernetes Engine (GKE) with Pub/Sub as a spooler. This allows ... | mlops-agent-with-gke |
---
title: Google
description: Deploying and monitoring DataRobot models on Google Cloud Platform (GCP) and Google Kubernetes Engine (GKE).
---
# Google {: #google }
The following sections describe techniques for integrating the Google Cloud Engine with DataRobot:
Topic | Describes...
----- | ------
[Deploy and mon... | index |
---
title: Real-time predictions
description: Use the DataRobot Prediction API for real-time predictions.
---
# Real-time predictions {: #real-time-predictions }
Once data is ingested, DataRobot provides several options for scoring model data. The most tightly integrated and feature-rich scoring method is using the P... | sf-client-scoring |
---
title: Snowflake
description: Guides around integrating DataRobot with Snowflake
---
# Snowflake {: #snowflake }
The articles in this section progress from data ingest to creating projects and machine learning models based on historic training sets, and finally to scoring new data through deployed models via sev... | index |
---
title: Snowflake external functions and streams
description: Use external API call functions to create a Snowflake scoring pipeline.
---
# Snowflake external functions and streams {: #snowflake-external-functions-and-streams }
With Snowflake, you can call out to [external APIs](https://docs.snowflake.com/en/sql-r... | sf-function-streams |
---
title: Data ingest and project creation
description: Retrieve data for Snowflake project creation.
---
# Data ingest and project creation {: #data-ingest-and-project-creation }
To create a project in DataRobot, you first need to ingest a training dataset. This dataset may or may not go through data engineering or... | sf-project-creation |
---
title: Server-side model scoring
description: Use the DataRobot Batch Prediction API for server-side model scoring.
---
# Server-side scoring {: #server-side-scoring }
The following describes advanced scoring options with Snowflake as a cloud-native database, leveraging the DataRobot Batch Prediction API from the... | sf-server-scoring |
---
title: Generate Snowflake UDF Scoring Code
description: Use the DataRobot Scoring Code JAR as a user-defined function (UDF) on Snowflake.
---
# Generate Snowflake UDF Scoring Code {: #generate-snowflake-udf-scoring-code }
Scoring Code makes it easy for you to perform predictions on DataRobot models anywhere you ... | snowflake-sc |
---
title: Large batch scoring
description: Use the DataRobot Batch Prediction API to mix and match source and target data sources.
---
# DataRobot and Snowflake: Large batch scoring and object storage {: #datarobot-and-snowflake-large-batch-scoring-and-object-storage}
With DataRobot's Batch Prediction API, you can c... | sf-large-batch |
---
title: Ingest data with AWS Athena
description: Ingest AWS Athena and Parquet data for machine learning.
---
# Ingest data with AWS Athena {: #ingest-data-with-aws-athena }
Multiple big data formats now offer different approaches to compressing large amounts of data for storage and analytics; some of these forma... | ingest-athena |
---
title: Path-based routing to PPS
description: Path-based routing to Portable Prediction Servers on AWS.
---
# Path-based routing to PPSs on AWS {: #path-based-routing-to-ppss-on-aws }
Using DataRobot MLOps, users can deploy DataRobot models into their own Kubernetes clusters—managed or Self-Managed AI Pla... | path-based-routing-to-pps-on-aws |
---
title: Score Snowflake data on AWS EMR Spark
description: Scoring Snowflake data using DataRobot Scoring Code on AWS EMR Spark.
---
# Score Snowflake data on AWS EMR Spark {: #score-snowflake-data-on-aws-emr-spark }
DataRobot provides exportable Scoring Code that you can use to score millions of records on Spar... | score-snowflake-aws-emr-spark |
---
title: AWS
description: Integrate DataRobot with Amazon Web Services.
---
# AWS {: #aws }
The sections described below provide techniques for integrating Amazon Web Services with DataRobot.
Topic | Describes...
----- | ------
[Import data from AWS S3](import-from-aws-s3) | Importing data from AWS S3 to AI Catal... | index |
---
title: Deploy models on AWS EKS
description: Deploy and monitor DataRobot models on AWS Elastic Kubernetes Service (EKS).
---
# Deploy models on AWS EKS {: #deploy-models-on-aws-eks }
With DataRobot MLOps, you can deploy DataRobot models into your own AWS Elastic Kubernetes Service (EKS) clusters and still have ... | deploy-dr-models-on-aws |
---
title: Import data from AWS S3
description: Import data from AWS S3 to start a DataRobot ML project.
---
# Import data from AWS S3 {: #import-data-from-aws-s3 }
This section shows how to ingest data from an Amazon Web Services S3 bucket into the DataRobot AI Catalog so that you can use it for ML modeling.
To ... | import-from-aws-s3 |
---
title: Monitor with serverless MLOps agents
description: Monitor models using serverless MLOps agents.
---
# Monitor with serverless MLOps agents {: #monitor-with-serverless-mlops-agents }
DataRobot can monitor model performance and drift statistics for models deployed on external systems. These externally deplo... | monitor-serverless-mlops-agents |
---
title: AWS Lambda reporting to MLOps
description: A serverless method of AWS Lambda reporting actuals to DataRobot MLOps.
---
# AWS Lambda reporting to MLOps {: #aws-lambda-reporting-to-mlops }
This topic describes a serverless method of reporting actuals data back to DataRobot once results are available for pre... | aws-lambda-reporting-to-mlops |
---
title: Use DataRobot Prime with AWS Lambda
description: Use DataRobot Prime to download a DataRobot model and deploy it using AWS Lambda.
---
# Use DataRobot Prime models with AWS Lambda {: #use-datarobot-prime-models-with-aws-lambda }
!!! info "Availability information"
The ability to create _new_ DataRobot... | prime-lambda |
---
title: Use Scoring Code with AWS Lambda
description: Learn how to integrate Scoring Code models with AWS Lambda.
---
# Use Scoring Code with AWS Lambda {: #use-scoring-code-with-aws-lambda }
This topic describes how you can use DataRobot’s Scoring Code functionality to download a model's Scoring Code and deploy ... | sc-lambda |
---
title: AWS Lambda
description: Integrate DataRobot with AWS Lambda.
---
# AWS Lambda {: #aws-lambda }
The sections described below provide techniques for integrating AWS Lambda with DataRobot.
Topic | Describes...
----- | ------
[Serverless MLOps agents](monitor-serverless-mlops-agents) | Monitoring external mo... | index |
---
title: Amazon SageMaker
description: Integrate DataRobot with Amazon SageMaker.
---
# Amazon SageMaker {: #amazon-sagemaker }
The sections described below provide techniques for integrating Amazon SageMaker with DataRobot.
Topic | Describes...
----- | ------
[Deploy models on Sagemaker](sagemaker-deploy) | Depl... | index |
---
title: Deploy models on SageMaker
description: Deploy models on SageMaker and monitor them with MLOps Agents.
---
# Deploy models on SageMaker
This article showcases how to make predictions and monitor external models deployed on AWS SageMaker using DataRobot’s [Scoring Code](scoring-code/index) and [MLOps agent... | sagemaker-deploy |
---
title: Monitor SageMaker models in MLOps
description: Monitoring a SageMaker-deployed model in DataRobot MLOps.
---
# Monitor SageMaker models in MLOps {: #monitor-sagemaker-models-in-mlops }
This topic outlines how to monitor an AWS SageMaker model developed and deployed on AWS for real-time API scoring.
DataRo... | sagemaker-monitor |
---
title: Use Scoring Code with AWS SageMaker
description: Using Scoring Code models with AWS SageMaker.
---
# Use Scoring Code with AWS SageMaker {: #use-scoring-code-with-aws-sagemaker }
This topic describes how to make predictions using DataRobot’s Scoring Code deployed on AWS SageMaker. Scoring Code allows you ... | sc-sagemaker |
---
title: Data Export tab
description: Export a deployment's stored prediction and training data to compute and monitor custom business or performance metrics.
---
# Data Export tab {: #data-export-tab }
You can export a deployment's stored training data, prediction data, and actuals to compute and monitor custom b... | data-export |
---
title: Usage tab
description: Tracks prediction processing progress for use in accuracy, data drift, and predictions over time analysis.
---
# Usage tab {: #usage-tab }
After deploying a model and making predictions in production, monitoring model quality and performance over time is critical to ensure the model... | deploy-usage |
---
title: Data Drift tab
description: How to use the Data Drift dashboard to analyze a deployed model's performance. It provides four interactive, exportable visualizations that communicate model health.
---
# Data Drift tab {: #data-drift-tab }
As training and production data change over time, a deployed model los... | data-drift |
---
title: Segmented analysis
description: Segmented analysis filters data drift and accuracy statistics into unique segment attributes and values to identify potential issues in your training and prediction data.
---
# Segmented analysis {: #segmented-analysis }
Segmented analysis identifies operational issues with... | deploy-segment |
---
title: Custom Metrics tab
description: Create and monitor up to 25 custom business or performance metrics.
---
# Custom Metrics tab {: #custom-metrics-tab }
On a deployment's **Custom Metrics** tab, you can use the data you collect from the [**Data Export** tab](data-export) (or data calculated through other cus... | custom-metrics |
---
title: Service Health tab
description: How to use the Service Health tab, which tracks metrics for how quickly a deployment responds to prediction requests to find bottlenecks and assess capacity.
---
# Service Health tab {: #service-health-tab }
The **Service Health** tab tracks metrics about a deployment's abi... | service-health |
---
title: Challengers tab
description: How to use the Challengers tab to submit challenger models that shadow a deployed model and replay predictions made against the deployed model. If a challenger outperforms the deployed model, you can replace the model.
---
# Challengers tab {: #challengers-tab }
!!! info "Avai... | challengers |
---
title: Performance monitoring
description: You can use monitoring tools to monitor deployed or remote models, data drift, model accuracy over time, and more.
---
# Performance monitoring {: #performance-monitoring }
To trust a model to power mission-critical operations, users need to have confidence in all aspec... | index |
---
title: Accuracy tab
description: How to use the Accuracy tab to determine whether a model's quality is decaying and if you should consider replacing it.
---
# Accuracy tab {: #accuracy-tab }
The **Accuracy** tab allows you to analyze the performance of model deployments over time using standard statistical measu... | deploy-accuracy |
---
title: Overview tab
description: Select a deployment from the Deployments page to view the Overview page.
---
# Overview tab {: #overview-tab }
When you select a deployment from the **Deployments** page (also called the *deployment inventory*), DataRobot opens to the **Overview** page for that deployment.
The ... | dep-overview |
---
title: Remote repository file browser for custom models and tasks
description: The remote repository file browser for custom models and tasks allows you to browse a remote repository from the Custom Model Workshop and select the files you want to pull into a custom model or task.
section_name: MLOps
maturity: publ... | pp-remote-repo-file-browser |
---
title: Extend compliance documentation with key values
description: Build custom compliance documentation templates with references to key values, adding the associated data to the template and limiting the manual editing needed to complete the compliance documentation.
section_name: MLOps
maturity: public-preview
... | model-registry-key-values |
---
title: Timeliness indicators for predictions and actuals
description: Enable timeliness tracking to retain the last calculated health status and reveal when status indicators are based on old data.
section_name: MLOps
maturity: public-preview
---
# Timeliness indicators for predictions and actuals
!!! info "Avai... | timeliness-status-indicators |
---
title: Automated deployment and replacement of Scoring Code in AzureML
description: Create a DataRobot-managed AzureML prediction environment to deploy and replace DataRobot Scoring Code in AzureML.
section_name: MLOps
maturity: public-preview
---
# Automated deployment and replacement of Scoring Code in AzureML {... | azureml-sc-deploy-replace |
---
title: Model package artifact creation workflow
description: The improved model package artifact creation workflow provides a clearer and more consistent path to model deployment with visible connections between a model and its associated model packages.
section_name: MLOps
maturity: public-preview
---
# Model pa... | pp-model-pkg-artifact-creation |
---
title: Runtime parameters for custom models
description: Add runtime parameters to a custom model through the model metadata.
section_name: MLOps
maturity: public-preview
---
# Runtime parameters for custom models
!!! info "Availability information"
Runtime parameters for custom models are off by default. Co... | pp-cus-model-runtime-params |
---
title: Monitoring jobs for custom metrics
description: To use custom metrics with external data sources, monitoring job definitions allow DataRobot to pull feature data and predictions from outside of DataRobot and into your defined custom metrics for monitoring on the Custom Metrics tab.
section_name: MLOps
maturi... | custom-metric-monitoring-jobs |
---
title: Automated deployment and replacement of Scoring Code in Snowflake
description: Create a DataRobot-managed Snowflake prediction environment to deploy and replace DataRobot Scoring Code in Snowflake.
section_name: MLOps
maturity: public-preview
---
# Automated deployment and replacement of Scoring Code in Sno... | pp-snowflake-sc-deploy-replace |
---
title: Custom model proxies for external models
description: Create custom model proxies for external models in the Custom Model Workshop.
section_name: MLOps
maturity: public-preview
platform: self-managed-only
---
# Custom model proxies for external models
!!! info "Availability information: Self-Managed only"... | pp-ext-model-proxy |
---
title: Versioning support in the Model Registry
description: Create registered models to provide an additional layer of organization to your model packages.
section_name: MLOps
maturity: public-preview
---
# Versioning support in the Model Registry {: #enable-versioning-support-in-the-model-registry }
!!! info "... | model-registry-versioning |
---
title: MLflow integration for DataRobot
description: Export a model from MLflow and import it into the DataRobot Model Registry], creating key values from the training parameters, metrics, tags, and artifacts in the MLflow model.
section_name: MLOps
maturity: public-preview
---
# MLflow integration for DataRobot
... | mlflow-integration |
---
title: Feature cache for Feature Discovery deployments
description: Schedule feature cache for Feature Discovery deployments
section_name: MLOps
maturity: public-preview
---
# Feature cache for Feature Discovery deployments {: #feature-cache-for-feature-discovery-deployments }
!!! info "Availability information"
... | safer-ft-cache |
---
title: Using the Tableau Analytics Extension with deployments
description: Use the Tableau analytics extension to integrate DataRobot predictions into your Tableau project.
section_name: MLOps
maturity: public-preview
---
# Using the Tableau Analytics Extension with deployments {: #using-the-tableau-analytics-exte... | tableau-extension |
---
title: MLOps public preview features
description: Read preliminary documentation for MLOps features currently in the DataRobot public preview pipeline.
section_name: MLOps
maturity: public-preview
---
# MLOps public preview features {: #public-preview-features }
{% include 'includes/pub-preview-notice-include.md'... | index |
---
title: Public network access for custom models
description: Access any fully qualified domain name (FQDN) in a public network so that the model can leverage third-party services, or disable public network access to isolate a model from the network and block outgoing traffic.
section_name: MLOps
maturity: public-pre... | cus-model-pub-network-access |
---
title: Model logs for model packages
description: View the model logs for a model package to see a history of the successful operations (INFO status) and errors (ERROR status).
section_name: MLOps
maturity: public-preview
---
# Model logs for model packages {: #model-logs-for-model-packages }
!!! info "Availabili... | pp-model-pkg-logs |
---
title: Service Health and Accuracy history
description: Service Health and Accuracy history allow you to compare the current model with previous models in one place, on the same scale.
section_name: MLOps
maturity: public-preview
---
# Service Health and Accuracy history {: #service-health-and-accuracy-history }
... | pp-deploy-history |
---
title: Multipart upload for the batch prediction API
description: Multipart upload for batch predictions allows you to override the default behavior to upload more than one file using multiple PUT requests and a POST request to finalize the upload process.
section_name: MLOps
maturity: public-preview
---
# Multipa... | batch-pred-multipart-upload |
---
title: MLOps reporting for unstructured models
description: Report MLOps statistics from custom inference models created with an unstructured regression, binary, or multiclass target type.
section_name: MLOps
maturity: public-preview
---
# MLOps reporting for unstructured models
!!! info "Availability information... | mlops-unstructured-models |
---
title: Deployment
description: Use DataRobot MLOps to deploy DataRobot models, as well as custom and external models written in languages like Python and R, onto runtime environments.
---
# Deployment {: #deployment }
With MLOps, the goal is to make model deployment easy. Regardless of your role—a business... | index |
---
title: Set up accuracy monitoring
description: Configure accuracy monitoring on a deployment's Accuracy Settings tab.
---
# Set up accuracy monitoring {: #set-up-accuracy-monitoring }
You can monitor a deployment for accuracy using the [**Accuracy**](deploy-accuracy) tab, which lets you analyze the performance o... | accuracy-settings |
---
title: Configure retraining
description: To maintain model performance after deployment without extensive manual work, enable Automated Retraining by configuring the general retraining settings and then defining retraining policies.
---
# Configure retraining {: #Configure-retraining }
To maintain model performa... | retraining-settings |
---
title: Enable data export
description: Enable prediction row storage for a deployment, allowing you to export the stored prediction and training data to compute and monitor custom business or performance metrics.
---
# Enable data export {: #enable-data-export }
You can enabled prediction row storage to activate... | data-export-settings |
---
title: Review predictions settings
description: The Predictions Settings tab provides details about your deployment's inference (also known as scoring) data.
---
# Review predictions settings
On a deployment's **Predictions > Settings** tab, you can view details about your deployment's inference (also known as s... | predictions-settings |
---
title: Configure challengers
description: Configure deployments using the Challengers tab to store prediction request data at the row level and replay predictions on a schedule.
---
# Configure challengers {: #configure-challengers }
DataRobot can securely store prediction request data at the row level for deplo... | challengers-settings |
---
title: Deployment settings
description:
---
# Deployment settings
After you [create and configure a deployment](add-deploy-info), you can use the settings tabs for individual features to add or update deployment functionality:
Topic | Describes
-------|------------
[Set up service health monitoring](service-h... | index |
---
title: Set up fairness monitoring
description: Configure fairness monitoring on a deployment's Fairness Settings tab.
---
# Set up fairness monitoring
On a deployment's **Fairness > Settings** tab, you can define [Bias and Fairness](fairness-metrics) settings for your deployment to identify any biases in a binar... | fairness-settings |
---
title: Set up humility rules
description: Configure humility rules which enable models to recognize, in real-time, when they make uncertain predictions or receive data they have not seen before. When they recognize conditions you specify in the rule, they perform desired actions you configure.
---
# Set up humili... | humility-settings |
---
title: Set up service health monitoring
description: Configure segmented analysis to access drill-down analysis of service health, data drift, and accuracy statistics by filtering them into unique segment attributes and values.
---
# Set up service health monitoring
On a deployment's **Service Health > Settings*... | service-health-settings |
---
title: Set up data drift monitoring
description: Configure data drift monitoring on a deployment's Data Drift Settings tab.
---
# Set up data drift monitoring {: #set-up-data-drift-monitoring }
When deploying a model, there is a chance that the dataset used for training and validation differs from the prediction... | data-drift-settings |
---
title: Deployment reports
description: Learn about the deployment reports, which summarize details of a deployment, such as its owner, how the model was built, the model age, and the humility monitoring status.
---
# Deployment reports {: #deployment-reports }
Ongoing monitoring reports are a critical step in th... | deploy-reports |
---
title: Governance lens
description: Learn about the Governance lens, which summarizes details of a deployment such as the owner, how the model was built, model age, and humility monitoring status.
---
# Governance lens {: #governance-lens }
The Governance lens summarizes the social and operational aspects of a ... | gov-lens |
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