The fastest way to operationalize AI at scale.
Deliver real-world results with incredible efficiency, flexibility and ease in any cloud, multi-cloud and at the edge.
Over 100 Chief Data Officers report these key challenges:
Easy-to-use SDK, UI, and API for fast, repeatable, low-code/no-code ML operations
Distributed computing core written in Rust-Lang uses up to 80% less infrastructure
Comprehensive audit logs, advanced model insights, full A/B testing
Designed for Data Scientists and ML Engineers alike
(As benchmarked with the ALOHA open-source model, various TF, XGBoost, and Computer Vision models in AWS, Azure, GCP, and Databricks)
Easily scale number of live models by 10X with minimal effort and via automation
Up to 12X faster inferencing, 80% lower cost, and free up 40% of AI team’s time
Continuously observe, optimize AI in production to get to value 3X faster
A Fortune 100 enterprise needed to deploy over 100 ML models to detect security breaches. Plus, the models had to be retrained and updated monthly.
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A medical device manufacturer was looking for a simple and scalable way to deploy and manage models to thousands of endpoint devices.
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One of the top 10 banks in the world needed to analyze billions of streaming events in real time while making it easy and fast to update cybersecurity models.
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An online food delivery firm wanted to know instantly when their fraud detection models started to drift.
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A large software company wanted to focus their sales team on the highest propensity prospects by generating better insights from calls.
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The Detroit Lions sought to maximize revenue for live event spot ticket prices during the course of the National Football League (NFL) season.
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The US Military needed to analyze petabytes of daily IoT data in the cloud and across millions of edge devices, including drones and ships, to swiftly detect security anomalies.
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One of the top 5 real estate investment trusts (REITs) in the U.S. needed to scale dynamic pricing to hundreds of regions and thousands of locations.
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An analytics and management software firm wanted to optimize their own customers’ complex buying journeys, integrating both first-party and third-party data into their analysis.
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