Description
What is PoplarML?
PoplarML is a platform that allows users to easily deploy production-ready and scalable machine learning (ML) systems with minimal engineering effort. It provides a CLI tool for seamless deployment of ML models to a fleet of GPUs, with support for popular frameworks like Tensorflow, Pytorch, and JAX. Users can invoke their models through a REST API endpoint for real-time inference.
How to use PoplarML?
To use PoplarML, follow these steps: 1. Get Started: Visit the website and sign up for an account. 2. Deploy Models to Production: Use the provided CLI tool to deploy your ML models to a fleet of GPUs. PoplarML takes care of scaling the deployment. 3. Real-time Inference: Invoke your deployed model through a REST API endpoint to get real-time predictions. 4. Framework Agnostic: Bring your Tensorflow, Pytorch, or JAX model, and PoplarML will handle the deployment process.
PoplarML Core Features
- Seamless deployment of ML models using a CLI tool to a fleet of GPUs
- Real-time inference through a REST API endpoint
- Framework agnostic, supporting Tensorflow, Pytorch, and JAX models
FAQ from PoplarML
PoplarML is a platform for deploying production-ready and scalable machine learning systems with minimal engineering effort.
To use PoplarML, sign up for an account and use the provided CLI tool to seamlessly deploy your ML models to a fleet of GPUs. You can then invoke your models through a REST API endpoint for real-time inference.
The core features of PoplarML include seamless deployment of ML models to GPUs using a CLI tool, real-time inference through a REST API endpoint, and support for popular ML frameworks like Tensorflow, Pytorch, and JAX.
PoplarML is suitable for deploying ML models to production environments, scaling ML systems with minimal engineering effort, enabling real-time inference for deployed models, and supporting various ML frameworks.
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