Deploy-XGBoost creates an API endpoint for your trained XGBoost model without you needing to be an expert at DevOps or cloud infrastructure. Save hundreds of developer hours.
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Bring your trained XGBoost model. Bring your requirements.txt. Get an endpoint. That's it.
Our abstractions on top of AWS, Docker and more take the manual work out of classical model deployment. Bring your trained model artifact. Bring your requirements.txt. Get an endpoint. That's it.
Unlock the power of stress-free XGBoost deployment, where an API appears soon after a model is trained. Our pricing plans are customizable and designed to cater a diversity of team sizes.
Best for students and tinkerers who train models in an ad-hoc manner using Jupyter Notebooks.
Ideal for teams within companies and startups that consistently deploy classical models into production.
Prudential Financial used XGBoost to retarget customers with a higher converting marketing channel.
Qualcomm deployed an XGBoost model that predicted the probability of a cellular modem delivering a signal in densely populated metropolitan area.
IBM Consulting, who was hired by a Fortune 100 CPG client, used XGBoost for customer segmentation.
