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.
Key Features
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.
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.
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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