Python client reference
- Advanced options
- Advanced tuning
- Anomaly assessment
- APIObject
- Application
- Automated documentation
- Batch job
- Batch monitoring
- Batch predictions
- Binary data helpers
- Blueprint
- Calendar file
- Challenger
- Champion model package
- Class mapping aggregation settings
- Client configuration
- Clustering
- Compliance documentation templates
- Confusion chart
- Credentials
- Custom metrics
- Custom models
- Custom tasks
- Data engine query generator
- Data exports
- Data slices
- Data store
- Database connectivity
- Dataset definition
- Datasets
- Datetime trend plots
- Deployment
- Document text extraction
- External baseline validation
- External scores and insights
- Feature
- Restoring Discarded Features
- Feature association
- Feature association matrix details
- Feature association featurelists
- Feature effects
- Feature lineage
- Featurelists
- Generative AI
- Insights
- Job
- Key-Values
- Lift chart
- Missing values report
- MLOps event
- Model job
- Recommended models
- Models
- Generative AI Moderation
- OCR job resources
- Pareto front
- Partitioning
- Payoff matrix
- Predict job
- Prediction dataset
- Prediction environment
- Prediction explanations
- Predictions
- PredictionServer
- Prime files
- Project
- Rating table
- Recipes
- Recipe Operations
- Registered models
- Registry jobs
- Relationship
- Relationships configuration
- ROC curve
- Ruleset
- Secondary datasets
- Secondary dataset configurations
- Segmented modeling
- SHAP
- Sharing access
- Sharing role
- Status check job
- Training predictions
- Types
- Use cases
- User blueprints
- Visual AI
- Word Cloud
The DataRobot Python client is a library for working with the DataRobot API. To access other clients and additional information about DataRobot’s APIs, visit the API documentation home.
The reference documentation outlines the functionality supported by the Python client. For information about specific endpoints, select a topic from the table of contents on the left.
To get started with the Python client, reference DataRobot’s API Quickstart guide. This guide outlines how to configure your environment to use the API.
You can learn about use cases and experiment with code examples using the Python client in the API user guide.
In addition to code examples and use cases, you can browse AI accelerators. AI accelerators are designed to help speed up model experimentation, development, and production readiness using the DataRobot API. They codify and package data science expertise in building and delivering successful machine learning projects into repeatable, code-first workflows and modular building blocks.