AI Accelerators Library

This accelerator shows how users can quickly and seamlessly enable LLMOPs or Observability in their existing Generative AI Solutions without the need of code refactoring.

A repeatable framework for a production pipeline from multiple tables. 

This accelerator is developed for use with Databricks to help users leverage the power of DataRobot for time-series modeling within their Databricks ecosystem.

Level up your end-to-end ML lifecycle on the Data Cloud  by integrating DataRobot and Snowflake.

This accelerator aims to provide instructions on how to build this type of system using DataRobot's generative AI solution framework. The accelerator shows how you can build a pipeline to create a knowledge base with only trusted research papers, and build a conversational agent that can answer questions from medical professionals.

Source data from S3 or Athena, build and evaluate ML models using DataRobot, send predictions back to S3.

Leverage popular python packages as well as DataRobot's python client  to recreate and augment lift chart visualizations.

Leverage DataRobot's python client to extract predictions and compute custom metrics.

How data stored in Azure can be used to train a collection of models.

Import data, build and evaluate models, and deploy a model into production to make new predictions with Snowflake.

Integrate DataRobot API, Papermill, and MLFlow to automate machine learning making it easier, robust, and easy to share.

Build and refine ML models within DataRobot and deploy them to run within AWS SageMaker.

Integrate directly into your GCP environment to accelerate your use of machine learning across all of the GCP services.

Pair the power of DataRobot with the Spark-backed notebook environment provided by Databricks.

Access, understand, and tune blueprints for both preprocessing and model hyperparameters.

Learn how to migrate a deployed model using from one DataRobot cluster to another of the same version.

Using Eureqa algorithm to discover the gravitational constant.

The building blocks for a time-series experimentation and production workflow.

Framework to compare several approaches for cold start modeling

Apply FIRE to your dataset and dramatically reduce the number of features.

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.

Import image files using Spark and prepare them into a data frame suitable for ingest

Leverage the power of machine learning to improve customer retention by  building a churn predictor app using Streamlit and DataRobot.

Understand all of your models in one place and more easily share your findings

Call the GCP API and enrich a modeling dataset that predicts customer churn

How to generate image features and aggregate numeric features for high frequency data sources. 

Sample solution for monitoring AWS Sagemaker models with DataRobot MLOps.

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.

Use DataRobot and the Python API to build a workflow with SAP as the remote data source.

Retrain policies with DataRobot MLOps demand forecast deployments.

Customize models on the leaderboard via Composable ML's API, the Blueprint Workshop.

Explore how to implement self-joins in panel data analysis

Bring external data from Ready Signal to help augment your time series forecasting accuracy

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.

Deep dive into the utilization of zero-shot text classification for error analysis in machine learning models.

Embed scoring code in a microservice and prepare as Docker container.

Identify clients who are likely to miss appointments and take action to prevent that from happening.

Build model factories leading to the mandatory requirement to significantly decrease training time.

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. 

Use Generative AI and Prompt Engineering to consume cluster insights and create cluster labels for DataRobot clusters.

Leverage open source optimization modules to further tune parameters in DataRobot blueprints.

Use Predictive AI models in tandem with Generative AI models and overcome the limitation of guardrails around automating summarization/segmentation of sentiment text.

Integrate LLM based agents like ChatGPT with DataRobot prediction explanations to quickly implement effective customer communication in AI based workflows. 

Leverage the power of DataRobotX to quickly run the AutoML workflow on the Lending Club Dataset.