Use cases and horizontal approaches¶
|Cold start demand forecasting workflow
|This accelerator provides a framework to compare several approaches for cold start modeling on series with limited or no history.
|End-to-end time series demand forecasting workflow
|Perform large-scale demand forecasting using DataRobot's Python package.
|Deploy a model in AWS SageMaker
|Learn how to programmatically build a model with DataRobot and export and host the model in AWS SageMaker.
|Demand forecasting and retraining workflow
|Implement retraining policies with DataRobot MLOps demand forecast deployments.
|Predictions for fantasy baseball
|Leverage the DataRobot API to quickly build multiple models that work together to predict common fantasy baseball metrics for each player in the upcoming season.
|Use Gramian angular fields to improve datasets
|Generate advanced features used for high frequency data use cases.
|Tackle churn before modeling
|Discover the problem-framing and data management steps required to successfully model for churn, using a B2C retail example and a B2B example based on a DataRobot’s churn model.
|Mastering tables in production ML
|Review an AI accelerator that uses a repeatable framework for a production pipeline from multiple tables.
|Netlift modeling workflow
|Leverage machine learning to find patterns around the types of people for whom marketing campaigns are most effective.
|Use feature engineering and Visual AI with acoustic data
|Generate image features in addition to aggregate numeric features for high frequency data sources.
|Demand forecasting with the What-if app
|Discover the problem framing and data management steps required to successfully model for churn, using a B2C retail example and a B2B example based on a DataRobot’s churn model.
|No-show appointment forecasting
|How to build a model that identifies patients most likely to miss appointments, with correlating reasons.
|Predict factory order quantities for new products
|Build a model to improve decisions about initial order quantities using future product details and product sketches.
|Build a recommendation engine
|Explore how to use historical user purchase data in order to create a recommendation model, which will attempt to guess which products out of a basket of items the customer will be likely to purchase at a given point in time.
|Use self-joins with panel data to improve model accuracy
|Explore how to implement self-joins in panel data analysis.
|Create a trading volume profile curve with a time series model factory
|Use a framework to build models that will allow you to predict how much of the next day trading volume will happen at each time interval.
|Predict performance degradation and service failure
|Use a predictive framework for managing and maintaining your machine learning models with DataRobot MLOps.
|Video object detection using Visual AI
|Learn how a deep learning model trained and deployed with DataRobot platform can be used for object detection on the video stream.
|Integrate speech recognition and machine learning
|Use Whisper to transcribe audio files, process them efficiently, and store the transcriptions in a structured format for further analysis or use.
|Run choice-based conjoint analysis
|Use a predictive framework for managing and maintaining your machine learning models using DataRobot MLOps.
|Use DataRobot predictions in mobile apps
|Learn how to incorporate DataRobot predictions into a mobile app
|Financial planning and analysis workflow
|This accelerator illustrates an end-to-end financial planning and analysis workflow in DataRobot.
|Use Causal AI with DataRobot
|Work with data recording hospital readmission outcomes for diabetes patients to evaluate the causal relationship between the diabetes patients' medication status and their subsequent chance of being readmitted to the hospital.
|Label data with the data annotator app
|Leverage the data annotator app to both label new data and label predicted data within an active learning situation after training a model with DataRobot.
Updated January 31, 2024
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