January 2023

Realize business value from AI more quickly with the January DataRobot release and new features that provide speed, flexibility, and ecosystem advancements. New, hosted Notebooks allow the development of AI/ML projects with code-first or code-free experiences. See 21% faster results* when choosing Quickrun Autopilot mode with DataRobot AutoML. Save time by building No-Code AI Apps directly from a model leaderboard. 

The new, user-friendly integration with custom model repositories with existing CI/CD tools like GitHub support ML Engineering teams’ automated workflows, while maintaining the DataRobot platform’s governance standards. 

This month, DataRobot also debuts Dedicated Managed AI Cloud on Microsoft Azure. This hosted version of the DataRobot AI Cloud platform is deployed for each customer in a dedicated and separate virtual private cloud that is operated, monitored, and maintained by DataRobot in-house experts. Already available on AWS and Google Cloud marketplaces, Dedicated Managed AI Cloud can now also be purchased on the Azure Marketplace.

Experience

New DataRobot Notebooks Streamline Code-First Development

New DataRobot Notebooks provides a fully managed, hosted platform with auto-scalable compute resources for creating and executing Jupyter-compatible notebooks. It is natively integrated with the DataRobot ecosystem to streamline code-first development across the ML lifecycle so that data science professionals spend more time on data science and less time on low-level configurations or infrastructure management. With built-in code intelligence like auto-completion, code-snippets for frequently used data science functions, and pre-installed ML libraries, data scientists are assured a holistic experience that encourages faster AI experimentation. 

Modeling

Quickrun Autopilot

See 21% faster results* when choosing Quickrun Autopilot mode with DataRobot AutoML. Get more value with the DataRobot improved Quickrun Autopilot. With faster iterative experimentation, users can more quickly finish each model build, adjust based on results, and launch the next experiment—all in less time. Get better results by running through more cycles of improvement.

*average in pre-release testing

5GB Large Dataset AutoTime Series Support

DataRobot Auto Time Series support for large datasets up to 5GB is now GA. This includes ingesting, running feature derivation, and modeling on datasets up to 5GB. The expanded capability makes it easier to sample the dataset without losing important Time Series-specific information. Additionally, the larger size supports more series for multiseries modeling.

Time Series Clustering Experience Improvements

There are now desirable experience improvements to Time Series clustering modeling for a more intuitive process. Improvement in the overall user experience includes changes in the GUI when setting up a Time Series clustering project.

Build No-Code AI Apps Directly from a Model Leaderboard

Generating a No-Code AI App for your business has never been easier, thanks to a unique new building process to the existing DataRobot No-Code AI Apps capability. In just a few clicks, you can create an application directly from the model leaderboard (summary information for each model built in a project). Streamlining this process helps generate insights quickly so that users can spend more time digging into the insights that matter most to the business and less time on application setup. In addition, it simplifies the creation process of No-Code AI Apps for people without MLOps experience. 

MLOps

GitHub Actions Custom Model Deployment

This user-friendly integration with custom model repositories in GitHub supports ML Engineering  teams’ automated workflows. DataRobot platform operations (e.g.,  retraining models, champion/challenger experiments, and custom deployments) integrate with a customer’s existing CI/CD (e.g., continuous integration and continuous development) tools for repeatability and reliability. As mature customers take a DevOps approach to data science, including CI/CD, this integration allows more automated custom (i.e., non-DataRobot) model deployments on DataRobot for code-first data scientists, while maintaining the DataRobot platform’s governance standards.

TTS/LSTM Batch Predictions Support

Make batch predictions with TTS/LSTM models with the same level of automation as other machine learning models. This provides an easier process to make predictions on TTS/LSTM (Traditional Time Series/Long-Short Term Memory Models) deployments. These types of models have many benefits, as they do not require frequent retraining and are easy to understand. However, without an intuitive way to make batch predictions, these models would not otherwise make it to production.

Visualize Feature Drift for Text Features as a Word Cloud

The word cloud is a quick and intuitive way to visualize data drift for text features for critical insight into how the input data has changed over time. It shows the terms that have drifted the least and the most using different colors and shades of colors, providing an easy interpretation for text feature drift in deployed models. This word cloud capability can be found on the data drift tab of a deployment, and is available for all text-based features that have been selected for data drift monitoring.

Enable Compliance Documentation for Models without Null Imputation

Generate compliance documentation for models that do not support null imputation using DataRobot Compliance Documentation. This is accessible as a new optional template alongside the preexisting standard Compliance Documentation template. The new Compliance Documentation without null imputation template will not include the “Sensitivity Analysis” section. This update makes the DataRobot Compliance Documentation offering inclusive of more models, (i.e. models that may not support null imputation). 

Infrastructure

Dedicated Managed AI Cloud on Microsoft Azure

Reducing time to value in deploying, upgrading, and managing the AI infrastructure, DataRobot offers Dedicated Managed AI Cloud on Microsoft Azure. This hosted version of the DataRobot AI Cloud platform is deployed for each customer in a dedicated and separate virtual private cloud and operated, monitored, and maintained by DataRobot in-house experts. Ideal for organizations with specific data sovereignty issues, DataRobot Dedicated Managed AI Cloud is available for Microsoft Azure customers and can be purchased using Azure credits. 

DataRobot AI Cloud – January 2023 Release Full Feature list

For the full details of features included in the DataRobot AI Cloud January 2023 Release, visit the DataRobot Documentation Release Center.