PoplarML
PoplarML, headquartered in San Francisco, facilitates the deployment of scalable machine learning systems with minimal engineering effort, supporting major frameworks like TensorFlow, Pytorch, and JAX.
About PoplarML
PoplarML, headquartered in San Francisco, CA, USA, is a B2B company specializing in engineering, product design, and technological solutions. Formerly known as Poplar, the company focuses on enabling the deployment of production-ready, scalable Machine Learning (ML) systems with minimal engineering effort. PoplarML serves industries across America and Canada, providing cutting-edge solutions that streamline the integration of ML into various business operations.
Services by PoplarML
PoplarML offers services designed to simplify the deployment of ML models. One of the primary services includes a one-click deployment feature that allows users to deploy models to a fleet of GPUs using a Command Line Interface (CLI) tool. The company also provides real-time inference capabilities through a REST API endpoint, enabling businesses to invoke models with ease. PoplarML supports framework-agnostic deployment compatible with popular ML frameworks such as TensorFlow, PyTorch, and JAX.
Y Combinator W23 Batch Participation
PoplarML participated in the Y Combinator W23 batch, which helped the startup gain access to mentoring, funding, and a broad network of investors and entrepreneurs. Y Combinator is known for fostering the growth of innovative companies, and PoplarML’s inclusion in this batch underscores its potential and capability in the competitive field of machine learning technologies.
Technological Solutions by PoplarML
PoplarML delivers technological solutions that focus on deploying ML models efficiently. The company’s one-click deployment tool is designed for users looking to deploy models to a fleet of GPUs with minimal configuration. Additionally, PoplarML’s real-time inference system enables models to be invoked via a REST API endpoint, ensuring quick and efficient data processing. This supports businesses in optimizing their ML operations without extensive engineering resources.
