Predictive Content Generator

The predictive content generator is a customizable app template for generating content using predictive model outputs. Real world use cases for this technology include:

• Using a next-best-offer predictive model to automatically draft personalized promotions.
• Using a credit risk model to automatically draft approval and rejection letters.

The predictive content generator highlights the combination of:

• Best-in-class predictive model training and deployment using DataRobot AutoML.
• Governance & hosting predictive & generative models using DataRobot MLOps.
• A shareable and customizable front-end for interacting with both predictive and generative models.

Warning

Application templates are intended to be starting points that provide guidance on how to develop, serve, and maintain AI applications. They require a developer or data scientist to adapt and modify them to meet business requirements before being put into production.

Table of Contents

1. Setup
2. Architecture overview
3. Why build AI Apps with DataRobot app templates?
4. Make changes
   • Change the LLM
   • Change the data and model training method
   • Modify the front-end
   • Change the language in the front-end
5. Share results
6. Delete all provisioned resources
7. Setup for advanced users
8. Data privacy

Setup

Important

If you are running this template in a DataRobot codespace, pulumi is already configured and the repository is automatically cloned; skip to Step 3.

1. If pulumi is not already installed, install the CLI following instructions here. After installing pulumi for the first time, restart your terminal and run:

   pulumi login --local  # omit --local to use Pulumi Cloud (requires separate account)

2. Clone the template repository.

   git clone https://github.com/datarobot-community/predictive-content-generator.git
   cd predictive-content-generator

3. Rename the file .env.template to .env in the root directory of the repo and populate your credentials. This template is pre-configured to use an Azure OpenAI endpoint. If you wish to use a different LLM provider, modifications to the code will be necessary. Please refer to the documentation inside .env.template

4. In a terminal, run the following command:

   python quickstart.py YOUR_PROJECT_NAME  # Windows users may have to use `py` instead of `python`

   Python 3.9+ is required.

Advanced users who want to control virtual environment creation, dependency installation, environment variable setup, and pulumi invocation, see the advanced setup instructions.

Architecture overview

App Templates contain three families of complementary logic. For this template, you can easily modify the front-end:

• AI Logic: Necessary to service AI requests, generate predictions, and manage predictive models.

  notebooks/  # Model training logic, scoring data prep logic
  

• App Logic: needed for user consumption; whether via a hosted frontend or integrating into an external consumption layer

  frontend/  # Streamlit frontend
  nbo/  # App biz logic & runtime helpers
  

• Operational logic: Necessary to turn on all DataRobot assets.

  infra/  # Settings for resources and assets to be created in DataRobot
  infra/__main__.py  # Pulumi program for configuring DataRobot to serve and monitor AI and App logic
  

Why build AI Apps with DataRobot app templates?

App templates transform your AI projects from notebooks to production-ready applications. Too often, getting models into production means rewriting code, juggling credentials, and coordinating with multiple tools and teams just to make simple changes. DataRobot's composable AI apps framework eliminates these bottlenecks, letting you spend more time experimenting with your ML and app logic and less time wrestling with plumbing and deployment.

• Start building in minutes: Deploy complete AI applications instantly, then customize AI logic or front-end independently - no architectural rewrites needed.
• Keep working your way: Data scientists keep working in notebooks, developers in IDEs, and configs stay isolated - update any piece without breaking others.
• Iterate with confidence: Make changes locally and deploy with confidence - spend less time writing and troubleshooting plumbing, more time improving your app.

Each template provides an end-to-end AI architecture, from raw inputs to deployed application, while remaining highly customizable for specific business requirements.

Make changes

Change the LLM

1. Modify the LLM setting in infra/settings_generative.py by changing LLM=GlobalLLM.AZURE_OPENAI_GPT_4_O to any other LLM from the GlobalLLM object.
2. Provide the required credentials in .env dependent on your choice.
3. Run pulumi up to update your stack (Or rerun your quickstart).

   source set_env.sh  # On windows use `set_env.bat`
   pulumi up

Change the data and model training method

1. Edit the notebook notebooks/train_model_nbo.ipynb which includes steps to import and prepare training data and configure settings to train models. The last cell of each notebook is required for the rest of the pipeline.
2. Run the revised notebook.
3. Run pulumi up to update your stack with the changes.

source set_env.sh  # On windows use `set_env.bat`
pulumi up

4. train_model_fraud.ipynb and train_model_underwriting.ipynb contain examples using this template for alternative use cases. You can run them once and call pulumi up to explore them. infra/settings_main.py can be updated to use these notebooks if you wish other collaborators to run these notebooks by default when first provisioning resources.

Modify the front-end

1. Ensure you have already run pulumi up at least once (to provision the deployment).
2. Streamlit assets are in frontend/ and can be directly edited. After provisioning the stack at least once, you can also test the front-end locally using streamlit run app.py from the frontend/ directory (don't forget to initialize your environment using source set_env.sh).

source set_env.sh  # On windows use `set_env.bat`
cd frontend
streamlit run app.py

3. Run pulumi up again to update your stack with the changes.

source set_env.sh  # On windows use `set_env.bat`
pulumi up

Change the language in the front-end

Optionally, you can set the application locale in nbo/i18n.py, e.g. APP_LOCALE = LanguageCode.JA. Supported locales are Japanese and English, with English set as the default.

Share results

1. Log into app.datarobot.com
2. Navigate to Registry > Application.
3. Navigate to the application you want to share, open the actions menu, and select Share from the dropdown.

Delete all provisioned resources

pulumi down

Then run the jupyter notebook notebooks/delete_non_pulumi_assets.ipynb.

Setup for advanced users

For manual control over the setup process adapt the following steps for MacOS/Linux to your environent:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
source set_env.sh
pulumi stack init YOUR_PROJECT_NAME
pulumi up 

e.g., for Windows/conda/cmd.exe the previous example would change to the following:

conda create --prefix .venv pip
conda activate .\.venv
pip install -r requirements.txt
set_env.bat
pulumi stack init YOUR_PROJECT_NAME
pulumi up 

For projects that will be maintained, DataRobot recommends forking the repo so upstream fixes and improvements can be merged in the future.

Data privacy

Your data privacy is important to us. Data handling is governed by the DataRobot Privacy Policy. Review the policy before using your own data with DataRobot.