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.

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how to use:
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.
Core freatures:
Seamless deployment of ML models using a CLI tool to a fleet of GPUsReal-time inference through a REST API endpointFramework agnostic, supporting Tensorflow, Pytorch, and JAX models
Use case:

Deploying ML models to production environments

Scaling ML systems with minimal engineering effort

Enabling real-time inference for deployed models

Supporting various ML frameworks

FAQ list:


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