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DataRobot API resources > API reference documentation

API reference documentation

The table below outlines the reference documentation available for DataRobot's APIs.

Resource Description
REST API: The DataRobot REST API provides a programmatic alternative to the UI for creating and managing DataRobot projects. It allows you to automate processes and iterate more quickly, and lets you use DataRobot with scripted control. The API provides an intuitive modeling and prediction interface. DataRobot customers can also access the legacy REST API docs. Note that accessing the legacy REST API docs requires you to be logged into the DataRobot application.
Open API specification: DataRobot customers can reference the OpenAPI specification for the DataRobot REST API, which helps automate the generation of a client for languages that DataRobot doesn't directly support. It also assists with the design, implementation, and testing integration with DataRobot's REST API using a variety of automated OpenAPI-compatible tools. Note that accessing the OpenAPI spec requires you to be logged into the DataRobot application.
Python client: Installation, configuration, and reference documentation for working with the Python client library.
R client: Installation, configuration, and reference documentation for working with the R client library.
Prediction API: Generate predictions with a deployment by submitting JSON or CSV input data via a POST request.
Batch prediction API: Score large datasets with flexible options for intake and output using the prediction servers you have deployed via the Batch Prediction API.

Code-first tools

The table below lists the various programmatic tools DataRobot has to offer in addition to the APIs.

Resource Description
Blueprint Workshop Construct and modify DataRobot blueprints and their tasks using a programmatic interface.
DataRobot Prediction Library The DataRobot Prediction Library is a Python library for making predictions using various prediction methods supported by DataRobot. It provides a common interface for making predictions, making it easy to swap out the underlying implementation.
DataRobotX (DRX) DataRobotX, or DRX, is a collection of DataRobot extensions designed to enhance your data science experience. DRX provides a streamlined experience for common workflows but also offers new, experimental high-level abstractions.
DataRobot User Models (DRUM) A repository that contains tools, templates, and information for assembling, debugging, testing, and running your custom inference models, custom tasks, and custom notebook environments with DataRobot.
MLOps agents The MLOps agents allow you to monitor and manage external models—those running outside of DataRobot MLOps. With this functionality, predictions and information from these models can be reported as part of MLOps deployments.
Management agent The MLOps management agent provides a standard mechanism to automate model deployment to any type of infrastructure. It pairs automated deployment with automated monitoring to ease the burden on remote models in production, especially with critical MLOps features such as challenger models and retraining.
DRApps DRApps is a simple command line interface (CLI) providing the tools required to host a custom application, such as a Streamlit app, in DataRobot using a DataRobot execution environment. This allows you to run apps without building your own Docker image. Custom applications don't provide any storage; however, you can access the full DataRobot API and other services.
DataRobot model metrics library A repository that contains a framework to compute model machine learning metrics over time and produce aggregated metrics. In addition, it provides examples of how to run and integrate this library with your custom metrics in DataRobot.
MLOps Utilities For Spark A utilities library to integrate MLOps tasks with Spark.
DataRobot MLFLow integration library A Python library that provides a means to export a model from the MLFlow model registry and push it to DataRobot's Model Registry. You can also review supporting DataRobot documentation.
DataRobot provider for Apache Airflow This quickstart guide on the DataRobot provider for Apache Airflow illustrates the setup and configuration process by implementing a basic Apache Airflow DAG (Directed Acyclic Graph) to orchestrate an end-to-end DataRobot AI pipeline.
Updated February 26, 2024