Over the past 12 years, I’ve been fortunate to discover what’s possible through innovation with AI, from being a graduate student at Cornell University to building the algorithms-based company Eureqa to leading a team of innovators at DataRobot. Since then, I’ve been more and more motivated to take what I’ve learned over the years and push these boundaries even further. For the past few months, I’ve been collaborating with Dom Divakarun, Product Manager for the Azure OpenAI service. I couldn’t be more excited to share what we’ve been working on with DataRobot and the Microsoft Azure OpenAI service.
Today, we’re introducing a new advanced integration with the Microsoft Azure OpenAI service. This integration, which uses the ChatGPT model in Azure OpenAI, provides a conversational AI experience that will allow you to directly interact with and interpret the model’s results and predictions. This important milestone is the first step in dramatically modernizing not only the development, but most importantly, the interpretation, understanding and adoption of AI use cases.
The integration of DataRobot and the Azure OpenAI service breaks down the barrier that has long existed between data teams and business stakeholders. This integration takes the power of one of the largest linguistic modeling technologies available today in the Azure OpenAI service and delivers value-driven results with machine learning through DataRobot.
Traditionally, developing relevant data science code and interpreting the results to solve a use case is done manually by data scientists. It is a time-consuming process that can slow the adoption of AI in an organization. However, we now take the information managed by DataRobot (eg data, features, models, predictions) and use the capabilities of the Azure OpenAI service to make it more accessible and understandable. The integration lets you build intelligent data science code that reflects your use case. For example, generating code for data preparation as well as model preparation and deployment. And it allows you to translate modeling results into key business results. An example of this is why a feature has a large impact on predictions. Data scientists still need to review and evaluate these results. However, data science teams can spend less time creating ML prediction interpretations, and business users can gain greater understanding from their ML applications. Finally, users benefit from a transparent and clear explanation of what ML predictions mean for them.
While I’m extremely excited about what this will mean for increasing the applications and impacts of AI, it’s just the beginning. Microsoft and DataRobot will work closely together to enhance the performance and reliability of these solutions, giving customers greater confidence to depend on insights.
This innovation is a testament to DataRobot’s relentless focus on developing pioneering solutions to launch customer AI projects for game-changing results. This is another example of how the DataRobot AI platform facilitates seamless integration with new technologies such as the Azure OpenAI service so you can build innovative business solutions using ML.
Accelerating value-driven AI with DataRobot and Azure OpenAI
So how does this happen? With this new approach, we’re creating a completely new experience in data science development and collaboration. DataRobot and Microsoft have injected new capabilities from big language models to predict the code that AI developers will need to write to solve a particular use case and translate the resulting statistical results into the business language needed to communicate with key business stakeholders and to cooperate.
For example, a data scientist can create data preparation code suitable for a use case, such as merging relevant data and deriving targets, automatically by describing the problem in natural language. This saves us the time it would otherwise take memorizing metadata and APIs.
Then, as a business user starts asking questions and analyzing insights, the DataRobot AI platform dynamically displays use case information, data and models, as well as analytics generated by the Azure OpenAI model, to generate textual descriptions of the most important observations. , and interpretations of their meaning. Not only are the models explained in business language, but the conversational capabilities of the Azure OpenAI service allow business stakeholders to ask follow-up questions and drill down on the most impactful findings.
It’s a revolutionary conversational experience that allows everyday people to interact with an ML model and its insights. New to data scientists, it helps translate model math into business impact, and equally helps business stakeholders get the answers they need to make changes.
Giving data scientists new power tools to work faster
As any data scientist knows, developing models and explaining the results is a time-consuming process. Coding involves memorizing APIs, debugging and fixing errors. Interpreting the results means translating what the features of the raw data represent and contextualizing the trends in the insights. While a data scientist may know the data by heart, AI-generated explanations also help others understand what various findings mean.
A unique user experience that combines DataRobot and the Azure OpenAI service streamlines and accelerates many of the repetitive tasks required for model development and implementation, such as notebooking and summarizing key results to stakeholders. Data scientists can quickly innovate to solve new ML problems and see the impact of their work on organizations. The integration also helps data scientists create new ways to articulate and explain ML models. Together, DataRobot and Azure OpenAI Service help create more actionable insights.
The potential of DataRobot and the Microsoft Azure OpenAI service
We’re just getting started. It’s a natural fit for Microsoft and DataRobot to work together. We will work together to implement complex generative AI strategies from Azure following DataRobot modeling strategies, opening up entirely new use cases for the enterprise.
A story rooted in innovation
DataRobot has been at the forefront of innovation in AutoML, MLOps, automated time series and feature engineering. I am personally excited about what the integration with Azure OpenAI service will mean for data science and our customers going forward.
We’ve been innovating for the past decade and we’re not done yet. Stay tuned for upcoming events. The DataRobot team works hard to push the boundaries of all new AI innovations to help organizations apply them to their organizations for value-driven AI.
See DataRobot and Azure OpenAI in action and learn more about DataRobot and Microsoft’s partnership. virtual event, From vision to value. Creating impact with AIlive or on demand.
About the writer
Chief Technology Officer of DataRobot
Michael Schmidt serves as DataRobot’s Chief Technology Officer, where he is responsible for advancing the next frontier of the company’s cutting-edge technology. Schmidt joined DataRobot in 2017 following the acquisition of Nutonian, a machine learning company he founded and led that has been instrumental in successful product launches including the Automated Time Series. Schmidt received his PhD from Cornell University, where his research focused on automated machine learning, artificial intelligence, and applied mathematics. He lives in Washington.
Meet Michael Schmidt