TurboML is a real-time machine learning platform that manages the entire production ML lifecycle, enabling users to process only new incoming data and continuously update models with live data. This approach reduces computational redundancies and has demonstrated cost savings of up to 45 times for clients like Grubhub and Etsy.
Funding
$30K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional machine learning (ML) platforms require repetitive processing of both new and previously processed data, leading to computational redundancies and increased costs. This inefficiency slows down experimentation, widens the gap between development and production, and hinders the ability to maintain models with continuously updated data.
Solution
TurboML is a real-time machine learning platform designed to manage the entire production ML lifecycle while optimizing computational costs. The platform focuses on processing only new, incoming data, eliminating the need to re-crunch previously processed information. By leveraging real-time data, TurboML accelerates experimentation, reduces the dev-to-prod gap, and ensures models remain fresh and effective through continuous updates. Its interoperable architecture integrates with existing data, analytics, and ML serving pipelines, allowing users to maintain their current infrastructure.
Target Audience
TurboML targets data scientists and machine learning engineers seeking to optimize costs, accelerate experimentation, and maintain continuously updated models within their existing infrastructure.
Features
- Complete ML platform for data ingestion, feature engineering, ML modeling, and post-deployment ML operations.
- Real-time data processing for accelerated experimentation and continuous model updates.
- Interoperable architecture using open protocols for seamless integration with existing data, analytics, and ML pipelines.
- Python and Jupyter support with no DSL, enabling immediate productivity.
- Flexible deployment options, including fully private VPC or on-prem deployments.
- Arbitrary Python scripting for user-defined features and algorithms, with access to lower-level streaming APIs.