DataRobot 7.2

Released September 13, 2021

In release 7.2, we have opened up DataRobot for data science experts who love to code via Composable ML and code-centric data preparation and pipelines. MLOps adds Continuous AI to keep production models at peak performance, bias monitoring to keep models fair, and new Decision Intelligence flows that let you apply business rules to each and every prediction.

Keep reading to understand more about our most exciting release yet.

Powerful Tools for Data Science Experts Who Love to Code Composable ML

Your Expertise Extended with Our World-Class Automation. To build best-in-class models, data scientists need to constantly experiment with data and algorithms. In reality, data scientists spend much of their time on repetitive or mundane tasks, like writing code for feature transformations or model operationalization, leaving them with little time to actually experiment.

New in release 7.2, DataRobot Composable ML provides customizable blueprints containing reusable building blocks that allow experts to save time on mundane coding tasks and focus on experimentation and other high-value activities. You can define your unique best-in-class machine learning algorithm, then combine it with built-in DataRobot’s capabilities to automate repetitive and laborious tasks, resulting in a dramatic

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DataRobot 7.1

Released June 15, 2021

As we reach the midpoint of 2021, we’re thrilled to announce our second major release of the year! In AutoML, automated feature discovery with push-down integration for Snowflake is now generally available. Automated Time Series now includes automatic data prep, and we have enhanced our unique Eureqa forecasting models. MLOps users can now perform major lifecycle operations on remote models using our new MLOps management agents. We’ve also introduced a brand-new No-Code AI App Builder that allows you to quickly create beautiful and powerful AI applications using a visual drag-and-drop user interface. No coding skills required.

And this is just a fraction of the release. Are you excited? So are we, so let’s jump in.

Automated AI Reports

Trusted Insights for Your AI Projects. Automated AI Reports are now available. AI reports are designed to summarize the most important findings of your modeling project to stakeholders in an easily consumable format. In just a few clicks you receive a comprehensive summary of your AI project. The report provides accuracy insights for the top-performing model, including speed and cross-validation scores. It also captures interpretability insights from the Feature Impact histogram for

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DataRobot 7.0

Released March 16, 2021

It’s our very first release of 2021, and we’re excited to share it with you. Release 7.0 of DataRobot, provides innovation across our entire platform through enhancements to all the products you know and love. We’ve improved our scoring tools in Data Prep, added image augmentation to Visual AI, introduced customizable compliance reports in AutoML and AutoTS, and added a way to easily compare your forecasting models to ours. In MLOps, we’ve added the ability to challenge any model built in any language or framework, and deployed to any environment. Here are the major headlines for this exciting new release.

Enhanced Prediction Preparation

Ultimate Flexibility For Scored Data. DataRobot’s self-service data preparation just got even more powerful in Release 7.0. Not only can you quickly and easily prepare your data for model training, you can also use our visual data prep capabilities to score new data after your models are built and use it for whatever purpose you choose. Since our data prep tools work seamlessly within our end-to-end AI Platform, we make it incredibly easy for anyone to get scored data from any deployed model. You can then integrate the scored data back into your production data pipelines or write it out to a huge variety of well-known enterprise data sources on-prem and in the cloud. All of the scored data is intelligently cached to accelerate the read performance of downstream applications. You can also get both SHAP and XEMP-based explanations for every single prediction, ensuring full transparency and trusted AI.

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