Rapid & simple ML
models deployment.

Rapid & simple ML
models deployment.

Transform your ML ideas into reality with instant deployment.


Don't mess with the Ops part - we do that for you.

Transform your ML ideas into reality with instant deployment.

 Don't mess with the Ops part - we do that for you.

Transform your ML ideas into reality with instant deployment.

 Don't mess with the Ops part - we do that for you.

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import cinnaroll


model_config = {

"project_id": "2eb823ea",

"model_object": net,

"model_input_sample": detect_cinnarolls,

"infer_func": infer,

"infer_func_input_format": "img",

"infer_func_output_format": "json",

"infer_func_input_sample": "cinnaroll.jpg",

"train_func": train

}


cinnaroll.rollout(model_config)

Deploy models with a simple config right in your Python code

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import cinnaroll


model_config = {

"project_id": "2eb823ea",

"model_object": net,

"model_input_sample": detect_cinnarolls,

"infer_func": infer,

"infer_func_input_format": "img",

"infer_func_output_format": "json",

"infer_func_input_sample": "cinnaroll.jpg",

"train_func": train

}


cinnaroll.rollout(model_config)

Deploy models with a simple config right in your Python code

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import cinnaroll


model_config = {

"project_id": "2eb823ea",

"model_object": net,

"model_input_sample": detect_cinnarolls,

"infer_func": infer,

"infer_func_input_format": "img",

"infer_func_output_format": "json",

"infer_func_input_sample": "cinnaroll.jpg",

"train_func": train

}


cinnaroll.rollout(model_config)

Deploy models with
a simple config right in your Python code

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import cinnaroll


model_config = {

"project_id": "2eb823ea",

"model_object": net,

"model_input_sample": detect_cinnarolls,

"infer_func": infer,

"infer_func_input_format": "img",

"infer_func_output_format": "json",

"infer_func_input_sample": "cinnaroll.jpg",

"train_func": train

}


cinnaroll.rollout(model_config)

Deploy models with a simple config right in your Python code

ab0ut cinnaroll.ai

Your machine learning models ready to be served.

Your machine
learning models
ready to be served.

Your machine learning models ready to be served.

Your machine learning models ready to
be served.

Configure deployment

Once your model is trained it will be versioned and registered in your project. Ready to be deployed.

Manage your models

Compare model metrics between versions, decide what model to deploy.

Serve and monitor

Send requests to the generated prediction endpoint. Test your model and monitor how it performs.

Product Video

Watch our demo

See for yourself how easy that is.

Pricing

Pay for inferences,
not for idling

Pay for inferences,
not for idling

Pay for inferences,
not for idling

Pay for inferences,
not for idling

Forever-free plan with 1000 seconds a month for your prediction requests included. $0.004 per every second past that. No credit card needed to start.

Top features

Why cinnaroll.ai?

All Pythonic configuration

All Pythonic configuration

All Pythonic configuration

Experiment management

Experiment management

Experiment management

Performance monitoring

Performance monitoring

Performance monitoring

Auto-generated prediction endpoints

Auto-generated prediction endpoints

Auto-generated prediction endpoints

Fast & secure API ready to go

Fast & secure API ready to go

Fast & secure API ready to go

Support for Tensorflow & pyTorch

Support for Tensorflow & pyTorch

Support for Tensorflow & pyTorch

Model training

Coming soon

Model training

Coming soon

Model training

Coming soon

Canary deployments

Coming soon

Canary deployments

Coming soon

Canary deployments

Coming soon

Simple setup

How does it work?

Configure.

Add model details, tracked metrics and prediction input sample to your script.

Run it and see your model versioned, well described and ready to be deployed.

Serve.

Choose the model version you want to serve.

You get an automatically built prediction endpoint. Ready for you to send requests. A fully functioning, fast, and stable API in minutes.

Testimonial

“I spend only 10% of my time developing models. The rest of it goes to preparing data and getting it all to run properly with infra and deployments. I’d very much like to focus on ML instead. And I love cinnamon buns.”

Magda, Senior Machine Learning Engineer @ Affirm

Ready to build some great ML?

Focus on running and testing your models, skip sidequests.

Ready to build some great ML?

Focus on running and testing your models, skip sidequests.

Ready to build some great ML?

Focus on running and testing your models, skip sidequests.

Ready to build some great ML?

Focus on running and testing your models, skip sidequests.