At Kernel ML, we focus on the transition from mathematical theory to functional code. We are a collective building the tools that sit between research papers and production environments.
Our work spans the entire machine learning lifecycle: from the core algorithms that define a model, to the services that allow it to scale in the real world.
- Packages – Modular libraries designed to be reusable and efficient.
- Research – Open repositories exploring neural architectures and optimization.
- Services – Production-ready patterns for deploying AI at scale.
- Experiments – Proofs-of-concept for testing new ideas quickly.
We build exclusively within the Python ecosystem. Our core work relies on PyTorch for research and modeling, and FastAPI for turning those models into resilient services. We use Docker and Kubernetes to ensure our implementations are portable and ready for the cloud.