At Polyaxon, we’re always looking for ways to push the boundaries of what’s possible with machine learning. That’s why we’re excited to introduce our new MLOps sandbox platform, which makes it easy to manage the entire lifecycle of your machine learning models on your local machine using Python or on docker/docker-compose.

Our new sandbox platform, called haupt, provides a comprehensive set of tools and services for building, training, tracking, and visualizing machine learning experiments in a simple way without any Kubernetes requirement.

With the new sandbox feature, you can easily track and manage your data, code, and model artifacts, allowing you to collaborate and reproduce experiments with ease.

One key feature of the new sandbox deployment feature is its support for a wide range of machine learning frameworks and libraries. Whether you’re using TensorFlow, PyTorch, or any other machine learning framework or library, Polyaxon sandbox makes it easy to perform the same tasks users would expect to do to manage high-quality machine learning models on their local machines.

In addition, experiments tracked and managed using the sandbox features can be packaged and synced back to a production Polyaxon deployment running on top of Kubernetes, which allows it to automatically scale up and down based on workload demands. This ensures that your machine learning pipelines always have the ease of debugging and speed of velocity using your local machine and the resources they need to run efficiently and effectively at scale when moving to production.

Ultimately, with Polyaxon’s new sandbox feature, we aim to streamline and accelerate your machine learning projects with the minimum infrastructure requirements, allowing you to quickly and easily put your models into production and start realizing the benefits of machine learning.

Polyaxon’s sandbox feature is in beta, you can try it today and see the power of machine learning in action.