TurboML is a machine learning platform designed for real-time applications, enabling organizations to manage the entire production ML lifecycle while leveraging real-time data.
TurboML provides tools and infrastructure for companies to develop, deploy, monitor, and maintain machine learning models in production. Its core value lies in supporting real-time data streams, which is crucial for businesses where immediate insights and actions are required. By focusing on real-time capabilities, TurboML aims to address the challenges of deploying and managing ML models in dynamic, data-driven environments.
The platform is tailored for software development teams and data scientists working in industries where the speed and reliability of machine learning outputs are business-critical. Typical users might include organizations in finance, e-commerce, and logistics that need to process large volumes of data and make split-second decisions based on predictive analytics.
How was TurboML started?
TurboML was founded by Siddharth Bhatia (Co-Founder and CEO) and Arjit Jain (Co-Founder and CTO). Their backgrounds in building scalable software systems and expertise in machine learning influenced the company’s focus on real-time ML operationalization.
Who uses TurboML?
TurboML primarily serves businesses and teams looking to operationalize machine learning workflows with a strong emphasis on real-time data processing. While specific customer names are not listed, the platform is positioned for innovative, data-driven organizations seeking robust ML lifecycle management.
Where is TurboML based?
TurboML is headquartered in San Francisco, United States, and operates as a privately held company with a team size of 11-50 employees.
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