DataRobot AI Platform 9.0

The DataRobot AI Platform is the only open, complete AI lifecycle platform leveraging machine learning that has broad interoperability, end-to-end capabilities for Experimentation and Production and can be deployed on-premises or in any cloud infrastructure. Exciting new features, a redesigned Experimentation user interface, new integrations with Snowflake and many more advancements make this a very exciting release for DataRobot customers.

Collaborative Experimentation Experience

By empowering teams to closely align with data, models, and subject matter experts, DataRobot offers Machine Learning Experimentation for model data preparation and model building. A newly designed experience interface, rethought user workflows, and an intuitive code-first and no-code/low-code experience for data science practitioners delivers faster iteration and experimentation. 


It is a new experience for collecting and managing use cases to comprehensively bring together business problem assets in one location. Workbench simplifies collaboration by enabling resource sharing in a single click, solving the issue of resources scattered across internal locations, hard drives, and GitHub repositories.

Workbench - DataRobot
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Data Preparation

Data Preparation designed specifically for ML data preparation, streamlining one of the most tedious and essential steps in AI/ML projects. Easily and quickly analyze, and transform structured data directly from Snowflake without compromising security, compliance, or financial controls.

Data Preparation - DataRobot
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Notebooks are fully managed, hosted, and embedded in the DataRobot AI Platform, giving data scientists flexibility to leverage code snippets, pre-installed dependencies and versioning.

Notebooks - DataRobot
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Value at Production Scale

Integrating production AI models with business workflows and applications, DataRobot aligns the models to be deployed near the critical data sources and business applications where decisions are made. DataRobot offers Machine Learning Production that scales value with new machine learning and automation capabilities for model testing and documentation, governance, model integration, and monitoring, using devops tools and best practices. 

Uniquely, DataRobot provides production capabilities regardless of whether the models are built in a standalone notebook or the DataRobot GUI, or where they are deployed—inside the DataRobot platform, in a data warehouse, or inside an enterprise application.

GitHub Marketplace Action for CI/CD

This is an advanced integration with custom model repositories in GitHub that supports ML Engineering teams to automate workflows, including custom (non-DataRobot) model deployments on DataRobot, while maintaining governance standards.

GitHub Marketplace Action for CI/CD - DataRobot
Learn more about GitHub Marketplace Action for CI/CD

Custom Inference Metrics

Uniquely embed your own analytics to calculate model metrics critical to your business, including novel drift or accuracy calculations. Add your business KPIs to the rich metrics already provided by DataRobot to fully track model performance.

Custom Inference Metrics - DataRobot
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Drift Management

Retain control even as global market conditions continue to dramatically change on a frequent basis. Track model drift, alert when a model should be retrained, explain why models may be drifting, and visualize data drift for text features, understanding how models change over time.

Drift Management - DataRobot
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Assured Compliance and Governance 

Today’s organizations need AI to be trusted, accountable, and governed. With DataRobot, you can set up automated and custom tests of model performance and automatically document any model’s behavior for compliance. With this, you can help ensure that models used in business-critical applications and workflows are managed to the highest standards, meet government regulations, and reduce overall risk from access or changes to models in production. 

Compliance documentation for external models

This feature saves data scientists hours of work by automatically creating compliance documentation for external models with the click of a button. Customization of compliance documentation helps data scientists adhere to enterprise or industry-specific requirements. Read more 

Compliance documentation for external models - DataRobot
Learn more about Compliance documentation for external models

MLflow Integration

Bring metadata from MLflow to the DataRobot platform. Expanding the existing DataRobot MLOps integrations, this MLflow work supports the need for AI teams to have flexibility and interoperability while also relying on the DataRobot AI Platform as a central location to manage a suite of models.  Read more 

MLflow Integration - DataRobot

Bias Mitigation tools

Identify and mitigate bias in individual models, building on the existing DataRobot Bias Management tools. As governments explore bias regulations, organizations are very interested in understanding and correcting model discrimination based on features, such as race, gender, or income. 

Bias Mitigation tools - DataRobot
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Broad Enterprise Ecosystem 

As enterprises make substantial investments in infrastructure, practitioner tools, data platforms and business applications, the DataRobot AI Platform is an open system supporting key integrations. Working in the context of your enterprise architecture, infrastructure, data platform and business applications, the DataRobot AI Platform integrates deeply with cloud data warehouses and data lakes so that you can analyze data, complete feature engineering, and deploy and monitor models. 

Extensive Snowflake integration

The integration, announced today with our longstanding DataRobot partner Snowflake, provides new functionality that enables you to work where you feel comfortable—in Snowflake or in the DataRobot AI Platform. You can now connect safely and seamlessly to Snowflake, the data cloud, wrangle and prepare data directly in Snowflake, and generate insights before the modeling process begins. Or you can start from the DataRobot interface to deploy, manage, monitor, and govern models that live in Snowflake. Now AI Builders can complete the ML lifecycle—from data prep to predictions—without repeated configuration.

Extensive Snowflake integration - DataRobot
Learn more about the Snowflake integration

SAP joint solutions

Get the right tools to quickly and safely build, deploy, and monitor ML models with both SAP data and data sources outside of SAP. DataRobot and SAP recently announced deeper alignment to conveniently build and deploy machine learning models across the SAP technology stack and deploy those models into SAP business applications. The joint solution complements the SAP embedded AI strategy, providing the right tools to quickly and safely build, deploy, and monitor ML models with both SAP data and data sources outside of SAP. 

Learn more about the SAP joint solutions

Kubernetes support

Kubernetes support, including Red Hat OpenShift and AKS, standardizes and simplifies installation. Kubernetes is now the standard run-time environment for cloud and on-premises infrastructure. DataRobot uses standard Kubernetes processes to automate the deployment of DataRobot services across multiple compute nodes, eliminating the time previously spent in doing this manually.  

Learn more about the Kubernetes support

Single-Tenant SaaS

Single-Tenant SaaS support lets you outsource the setup and management of the DataRobot AI Platform to DataRobot experts, using a robust, secure, single-tenant hosted solution available on Amazon AWS, Microsoft Azure, or Google Cloud. DataRobot can even be purchased with existing cloud credits, simplifying the procurement process.

Learn more about Single-Tenant SaaS

DataRobot AI Platform 9.0 Release Full Feature List

For the full details of features included in the DataRobot AI Platform 9.0 Release, visit the DataRobot Documentation Release Center.