Main Purpose
The main purpose of OctoML is to provide a machine learning deployment platform that offers choice, automation, and performance for organizations deploying their trained models into production.
Key Features
- Model Optimization: OctoML brings together AI, systems, programming languages, compilers, and architecture to optimize and deploy machine learning systems.
- Octomizer: OctoML's flagship product, Octomizer, enables engineering teams to deploy models in hours on any hardware, cloud provider, or edge device.
- Framework and Hardware Support: Octomizer supports a wide variety of ML frameworks like PyTorch, TensorFlow, and ONNX serialized models, as well as hardware backends like NVIDIA/CUDA, x86, AMD, ARM, Intel, MIPS, and more.
Use Case
- Organizations: OctoML is designed for organizations that want to optimize and deploy their machine learning models efficiently and effectively, regardless of the hardware or cloud provider they use.
