Learning sessions are a popular resource in the community. To make them easy to find, we've created this quick index. And, our SMEs are always interested in hearing ideas for new learning sessions! If you have a burning AI/ML/DR topic that you don't see covered already, please click Comment and send it along!
AI Problem Framing—Properly framed AI use cases are most effective at guiding businesses through current and future decision-making opportunities. This learning session presents considerations for framing effective AI use cases.
Change Management in the AI Space—Community DataRobot experts discuss the importance of change management in machine learning, best practices, and more. Join this learning session and learn tips for managing change in your business.
AI in the Retail Industry—Retail trends and how AI as an enabling technology can transform the performance of a retail business.
Model Building sessions
Automated Feature Engineering with Feature Discovery—Feature engineering is important and often time-consuming and even challenging. This learning session discusses some of the common FE techniques and how DataRobot's Feature Discovery tools help uncomplicate FE.
Visual AI—You've built your first set of image models using DataRobot's new Visual AI capability. So, now what comes next? How can you improve model accuracy? How can you understand what the models are learning? And, all of the built-in Image Insights look cool—how can you use them to drive modeling decisions? This learning session explains how to best leverage Visual AI in your machine learning projects.
Model Explainability with SHAP in DataRobot—Understanding how a model makes its predictions helps you trust the model. This session introduces DataRobot's use of SHAP values (Shapley values) which help explain models and how they arrive at their predictions.
Trust Bias in AI—Discussion of the theoretical understanding of bias and fairness, and demonstration of how DataRobot can help you tackle AI bias.
Improving Time Series Models—Tips and tricks for developing highly effective and competitive machine learning models for time series forecasting.
DataRobot Location AI for AutoML—Introduction to DataRobot's Location AI capabilities and explanation of the industries and use cases that stand to benefit immediately from the emerging use of geospatial analysis in their machine learning workflows.
Monitoring All Your Models with DataRobot Agents—Do you have machine learning models that are running outside of DataRobot? Is your organization using a set of diverse tools and platforms to deploy models, despite what IT wants? This session introduces DataRobot's MLOps agent framework which allows anyone to monitor models deployed externally, to DataRobot MLOps platform.