A repeatable framework for a production pipeline from multiple tables.
The building blocks for a time-series experimentation and production workflow.
Framework to compare several approaches for cold start modeling
Problem framing and data management steps required before modelling begins
Adjust certain known in advance variable values to see how changes in those factors might affect the forecasted demand.
Train a model on historical customer purchases in order to make recommendations for future visits.
How to generate image features and aggregate numeric features for high frequency data sources.
Build a model to improve decisions about initial order quantities using future product details and product sketches.
Learn how to use Gramian Angular fields to improve performance on high frequency datasets.
Retrain policies with DataRobot MLOps demand forecast deployments.
Explore how to implement self-joins in panel data analysis
Isolate the impact of a marketing campaign on specific prospective customers’ propensity to purchase something.
Build models that will allow prediction of how much of the next day trading volume will happen at each time interval.
Identify clients who are likely to miss appointments and take action to prevent that from happening.
Develop a powerful predictive model that utilizes historical customer and transactional data, enabling us to identify suspicious activities.
Leverage the DataRobot API to build multiple models that work together to predict common fantasy baseball metrics for each player in the upcoming season.
Leverage the power of DataRobotX to quickly run the AutoML workflow on the Lending Club Dataset.