From ML model to web app, in minutes.

Booklet adds an elegant web interface and powerful data integrations to your deployed Machine Learning models, all with no code changes. It's the engineering team your models deserve.

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How Booklet Works

When connected to your deployed ML model, Booklet creates a beautiful user interface and easily integrates your model into your existing services.

Connect your models

Point Booklet to your AWS Sagemaker, AzureML or custom models with a few simple configurations.

Setup the web UI

Describe your model inputs, configure the output template and we'll instantly create a Web UI frontend for your model.

Build a dataflow

Setup by connecting to AWS Redshift, Intercom, Mailchimp, and more. In minutes, you can launch and monitor dataflows that make your model powerful.

Live demos

Try it now! We've connected Booklet to a few live ML models below.

Iris Data Prediction

Using the classic Iris dataset, we built a model to predict Iris species. Test out the model and trigger a dataflow from the AWS Redshift dataset we've connected.

Sales Lead Scoring

To help a sales team better prioritize potential customers, lead scoring identifies high-value customers. Read more about this model in our documentation and send results to a CRM.

PyTorch Text Classification

Test out a PyTorch Text Classification Neural Network, trained with categorized news headlines. This model returns a list of categories and their probabilities for a given text string.

COVID-19 Project Recommender

Use this tool to find an open source COVID-19 project to contribute to on GitHub. This recommendation system was trained with a list of COVID-19 repos from GitHub.

Key Features

Connect

Gather ML Models in One Place

Quickly reference and organize all model endpoints in one dashboard. We currently support AWS Sagemaker, Azure ML, custom endpoints and many more!

No Code Changes. There's no libraries to install or new code patterns to master. Just point Booklet at your existing model endpoint.

Demo

Show off your Models with a UI

No more trial and error with curl. Just describe your model inputs and we'll build the inference UI. Presenting your model is about to get a lot more satisfying.

Custom Post-Processing. There are infinite output possibilities with Booklet's custom post-processing. By utilizing the Nunjucks template language, both simple and complex outputs are within reach.

Dataflows

Setup ML Dataflows

With a few clicks, quickly connect models to inputs and destinations. You can create scored datasets, infuse machine learning into 3rd party tools and so much more.

Smart error handling. Databases go offline, messy data is created, and destinations change. Booklet ensures your dataflows retry on error and alert you when things fail.

Get started - it's free!

You are minutes away from wrapping your ML Model in a full-fledged web app.

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Like what you see, but don't have the Data Science support? Contact services@booklet.ai to learn about our services.