AI Accelerators Library

This accelerator shows how you can use Visual AI on geospatial data. Instead of deriving numeric features from the georeferenced data, you look at the geospatial data as images. For example, if you have a map of population distribution, instead of extracting the population that corresponds to each row of the main table you can pass the region of the map that corresponds to that row. 

This notebook serves as a comprehensive guide to the intricate world of insurance pricing, leveraging historical claims data for modeling and analysis. The primary objective of this notebook is to enable insurance professionals and data scientists to predict insurance pricing accurately and efficiently with DataRobot platform.

DataRobot Visual AI  allows you to train deep learning models intended for the Computer Vision projects that are demanded by the different industries. The object detection (binary and multiclass classification) applied to image and video processing is one of the tasks that can be easily and efficiently implemented with the DataRobot Visual AI. You can also bring your own Computer Vision model and deploy it in DataRobot via Custom Model Workshop .

An AI model can't just be an experiment. AI Predictions need to be in the hands of real users interactive with customers, products, or users. This accelerator demonstrates how to incorporate DataRobot predictions into a mobile app.

This accelerator demonstrates the use of DataRobot custom models functionality to deploy a speech recognition capability to DataRobot based on the OpenAI Whisper models (currently uses the "base" model). This allows the capability to leverage the DataRobot environment and resources, on cloud or on prem.

Conjoint analysis is widely used tool used in marketing research for new product development testing. It's usually executed as an online survey format with a few survey respondents will make 1 choice out of a set of different alternatives. The output allows researches to accurately identify what product features and combination works best before developing them.

This AI Accelerator demonstrates how to reconcile (e.g., post-processing to sum appropriately) independent time series forecasts with a hierarchical structure. Reconciling, also known as making "coherent" forecasts , is often a requirement when submitting hierarchical forecasts to stakeholders. This notebook leverages the increasingly popular HierarchicalForecast  python library to do the reconciliation on forecasts generated from DataRobot time series deployments. 

This tutorial introduces a workflow utilizing OpenAI's Whisper model, a cutting-edge speech recognition system. Whisper excels in transcribing a diverse range of audio types, even with varying accents, converting spoken language into precise written text efficiently.

This accelerator shows how to call a Flask app to easily annotate image data for an ML model.

The notebook demonstrates how to leverage DataRobot MLOps functionality to predict if a machine learning model is likely to degrade within a specific time period and if infrastructure failures may occur.

Predictive AI models are a powerful tool for uncovering subtle predictive relationships between observed variables. But sometimes, you need to draw conclusions about the causal relationship between two variables, not just the observed association. To achieve this "Causal AI", you can use the DataRobot platform and a quasi-experimental technique called "Inverse Propensity of Treatment Weighting". This notebook will apply this technique to data on diabetes hospital patient readmission.

This notebook illustrates an end-to-end demand FP&A workflow in DataRobot. Time series forecasting in DataRobot has a huge suite of tools and approaches to handle highly complex multiseries problems.

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.