This talk introduces Polyaxon, an open source platform on Kubernetes for training, monitoring, and reproducing machine learning and deep learning applications.
Building modern machine learning systems involve various components and often requires connecting and managing different services, which introduces huge barriers of complexity in adopting machine learning. Infrastructure engineers and data scientists will often spend a significant amount of time manually tweaking deployments. Polyaxon is an extensible open source machine learning stack on Kubernetes that addresses these concerns.
Polyaxon is built on Kubenernetes to make machine learning, reproducible, scalable, and portable. It uses the container technology to abstract any dependencies required for the process of the experimentation. And it has a built-in versioning mechanism for code, data, and experiments.
The goal is to provide a stack that is easy to use and at the same time extensible, to enable data scientists to iterate faster on their algorithms.