This accelerator walks through all of the new DataRobot generative AI capabilities and serves as a template for code-first users. It showcases how to create and use playgrounds, connect vector databases and LLMs, and how to query and compare them. It uses the official DataRobot documentation as an example. Furthermore, it combines DataRobot's well-known custom model capablities to allow users to bring their own LLMs and vector databases. It shows how to link, use, and deploy them together with the ones offered by DataRobot.

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This AI Accelerator demonstrates how to implement a Streamlit application based on Google Gemini LLM and host it on DataRobot platform. The user of this AI Accelerator is expected to be familiar with the custom model deployment process and custom metrics creation in DataRobot as well as with Google Vertex AI.

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There are a wide variety of LLM models, for example, OpenAI (not Azure), Gemini Pro, Cohere and Claude. Managing and monitoring these LLM models are crucial to use them. Data Drift monitoring by DataRobot MLOps enable us to detect the changes the user prompt and its responses and notify us that user might use different as AI builder expected initially. Sidecar models prevent the Jailbreak or replace Personally Identifiable Information or evaluate LLM response by our global model in the model registry or your created models. Data Export functionality reminds us of what the user desired to know at each moment or of necessary data you should be included in RAG system. Custom Metrics indicates your own KPI which you can make your decision e.g. token costs, toxicity and Hallucination.

 

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Microsoft Teams offers workspace chat and video conferencing, file storage, and application integration to organizations. Workspace chat feature aloow you to interact with other users and bots in their day-to-day activities. This feature is useful for deploying Generative AI agents to improve employee productivity. With DataRobot's Generative AI offerings, organizations can deploy chatbots without the need for an additional front-end or consumption layers.

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Machine learning models have biases using small data, and some industries such as health care and manufaturing lack labled data. In light of this, a good approach is to select robust features to build models.

 

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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. 

 

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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.

 

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This accelerator shows how users can apply Local Interpretable Model-agnostic Explanations (LIME) to models built and deployed with DataRobot. LIME serves as another method in your toolbox to explain model predictions, complementing the built-in DataRobot capabilities of XEMP and SHAP prediction explanations.

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There are a wide variety of open source models. For example, there has been a lot of interest in LLama and variations such as Alpaca or Vicuna, Falcon, Mistral etc. Hosting these is a challenge as they require GPUs which are expensive so often customers want to compare cloud providers to find the best hosting option to meet their own needs. In this example we will work with Google Cloud Platform.

 
 
 
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This notebook presents an example workflow to create one-way and two-way partial dependence plots (PDP), and Individual Conditional Expectations (ICE) insights using DataRobot

 

This notebook has two parts:

  1. Where we score data against a deployment, and join the predictions back with the full dataset, and
  2. Where we use the scored dataset to gain insights by generation PDP and ICE plots
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Modern machine learning techniques are now capable of assisting manufacturers streamline their product development in numerous ways. In this notebook, we are going to focus on detecting and classifying product defects using state of the art computer vision systems. Utilizing machine learning brings immense value to manufacturers, transforming their production processes and giving them an overall competitive edge. By leveraging these advanced methods, manufacturers can streamline product development, enhance defect detection accuracy, optimize operational efficiency, reduce costs, and ultimately deliver higher-quality products to meet the ever-growing demands of the market.

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This note book provides a user with a method of changing the output of User Activity Monitor to allow the user to drop an entire column of output or change the contents of that column in a way to preserve the anonymity of the column but maintain consistency for reporting.

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This notebook is intended for a GraphQL developer who wants to integrate with DataRobot.

In this is example implementation, a GraphQL server is connecting to the DataRobot OpenAPI specification using GraphQL Mesh, the currently maintained option.

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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 .

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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.

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This accelerator shows an app to create synthetic training data

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This notebook presents an example workflow for carrying out statistical tests, notify stakeholders of any issues via Slack, and generate automated compliance documentation with the test results.

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In this notebook, we implement a very simple model based on the Q-learning algorithm. This notebook is intend to show a basic form of Reinforcement Learning that doesn't require a deep understanding of neural networks or advanced mathematics and how one might deploy such a model in DataRobot.

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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.

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There are a wide variety of open source models. For example, there has been a lot of interest in LLama and variations such as Alpaca or Vicuna, Falcon, Mistral etc. Hosting these is a challenge as they require GPUs which are expensive so often customers want to compare cloud providers to find the best hosting option to meet their own needs. In this example we will work with Google Cloud Platform.

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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.

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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. 

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The use case provided in this notebook takes the latest update of an RSS feed from CNN, downloads the article as text, embeds the text into a vector database, uses Google Bison to summarize the text, and provides a summary of the article in a streamlit app.

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The use case provided in this notebook creates synthetic training data sets for use in DataRobot models. This notebook outlines how to create a synthetic training data set in a csv file, with name, address, phone number, company, account number, and credit score.

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This AI Accelerator demonstrates various ways for generating prediction intervals for any DataRobot model. The methods presented here are rooted in the area of conformal inference  (also known as conformal prediction ). These types of approaches have become increasingly popular for uncertainty quantification because they do not require strict distributional assumptions to be met in order to achieve proper coverage (i.e., they only require that the testing data is exchangeable with the training data).

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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.

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This notebook presents some examples for taking a datarobot project and exporting its model insights as both machine readable files and plots in various file formats.

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t-SNE (t-Distributed Stochastic Neighbor Embedding) is a powerful technique for dimensionality reduction that can effectively visualize high-dimensional data in a lower-dimensional space. Dimensionality reduction can improve machine learning results by reducing computational complexity of the algorithms, preventing overfitting, and focusing on the most relevant features in the dataset.

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

 

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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.

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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.

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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.

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This accelerator outlines how to create, deploy, and monitor a custom inference model with DataRobot's Python client. You can use the Custom Model Workshop to upload a model artifact to create, test, and deploy custom inference models to DataRobot’s centralized deployment hub.

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Retrieval Augmented Generation (RAG) has become an industry standard method for interfacing with large language models by making them 'context aware'. However, there are a number of situations where a text generation problem is not solved by interacting with large vector database containing many documents. These problems require context but where the context is not known before query time and is often unrelated to existing vector stores. Usually, they are questions about single documents where desirable behavior is to allow the document to be specified at runtime.

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This accelerator aims to assist DataRobot trial users by providing a guided walkthough of the trial experience. DataRobot suggests that you complete the Flight Delays sample use case in the graphical user interface first, and then return to this accelerator.

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

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This accelerator aims to illustrate how businesses can use DataRobot to effectively and holistically monitor generative AI solutions, using the metrics relevant to them.

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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.

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In this accelerator, we will illustrate how to use Generative AI models to cater to Level 1 requests, allowing support teams to focus on more pressing and high visibility requests. Learning from historical communications, Generative AI Agents can maintain the same standard of support communication that the customers are used to. 

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

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A repeatable framework for a production pipeline from multiple tables. 

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

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

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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.

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

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

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

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

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

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

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About AI Accelerators
Discover code-first, modular building blocks for efficient model development and deployment that provide a template for kick-starting a project with DataRobot.

Check out GitHub to learn how to get started.