![CA-based-Baseten-Secures-40Million-in-Series-B-Round-Funding CA based Baseten secures $40million in series B round funding. Leading the round were IVP and Spark Capital, with participation from Greylock, South Park Commons, Lachy Groom, and Base Case, among other current investors.](https://startuprise.io/wp-content/uploads/2024/03/CA-based-Baseten-Secures-40Million-in-Series-B-Round-Funding.webp)
![CA-based Baseten secures $40million in series B round funding. Leading the round were IVP and Spark Capital, with participation from Greylock, South Park Commons, Lachy Groom, and Base Case, among other current investors.](https://startuprise.io/wp-content/uploads/2024/03/CA-based-Baseten-Secures-40Million-in-Series-B-Round-Funding.webp)
CA-based Baseten secures $40million in series B round funding. Leading the round were IVP and Spark Capital, with participation from Greylock, South Park Commons, Lachy Groom, and Base Case, among other current investors.
The money will be used by the business to grow both its operations and growth initiatives. Baseten, a 2019 startup, offers a framework for running machine learning workloads on user clouds.
Users don’t have to worry about observability, roll-out, or version management because of its inbuilt workflows, which support huge models in production. It powers fundamental machine learning workloads for businesses like Robust Intelligence, Descript, Writer, Patreon, Picnic Health, and Loop.
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Support for numerous clouds, allowing customers’ workloads to be distributed across several locations and clouds. connections with top-tier runtimes, such as TensorRT. relationships with AWS and GCP, allowing clients to get the greatest gear without having to spend months haggling with cloud providers about compute.
About Baseten
At Baseten, they offer all the necessary infrastructure for the effective, scalable, and economical deployment and serving of machine learning models. Get going in a matter of minutes and stay out of trouble with convoluted deployment procedures.
Best-in-class open-source models can be implemented, and you can benefit from efficient serving for your own models. They also leverage light-speed inference on infra that autoscales with your traffic, together with horizontally scalable services that take you from prototype to production.
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