Reinvented for Real-Time
Iterate faster by experimenting directly on live data.
Maintain and enhance ML with continuous fresh data updates.
Why do you need TurboML to optimize your
Make ML fast enough to increasingly shift towards on-demand compute, thereby reducing redundancies.
Optimize costs by processing only new incoming data, thus eliminating the need to repeatedly crunch through previously processed information.
Case studies from Grubhub and Etsy show direct cost savings of up to 45 times!
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TurboML at a Glance!
Complete ML platform
One single platform for all your ML needs, from data ingestion, to feature engineering, to ML modeling, to all post-deployment ML operations.
Built for real-time
Leverage real-time data to accelerate experimentation, iterate faster, reduce the dev-to-prod gap, and keep models fresh and effective with continuous updates.
Meets you where you are
Interoperable architecture using open protocols to integrate with your stack, so you can continue using the same data, analytics and ML serving pipelines.
Easy to use
Familiar Python and Jupyter, absence of DSL, and no prerequisites on data streaming mean zero learning curve, so you can be productive from day one.
Deployment tailored for you
Our batteries-included architecture is loosely coupled, so you can use your own infra wherever needed, including fully private VPC or on-prem deployments.
Programmable all the way
Arbitrary python scripting for user-defined features and algorithms, along with access to lower level streaming APIs, offers complete control to innovate.
