Today, Polyaxon gives you a simple interface to access data, train experiments, and manage models.
What most people don't know is that our long-term vision is to create an enterprise-grade platform that makes it faster, easier, and more efficient to develop machine learning and deep learning applications, in every industry, in every organization, in every country, and not just the ones working at the half-dozen tech companies. We believe that by using the power of machine learning, individuals and organizations will change the world. We want to help and simplify this technological transition, and we want to enable the innovations needed in transportation, health care, energy, communication, space travel, etc.
The process of creating Machine learning is still poor, duplicative, manual, and single-player. Every machine learning project is about integrating components of variable quality developed mostly in isolation, with a high chance of failure. We want to solve this problem, by developing a platform that enables:
- Easier on-boarding.
- Better knowledge distribution and collaboration.
- Improved productivity.
- Faster innovation cycle.
- Easier workflows.
Polyaxon's short term vision is to enable solo-researchers, teams, and organizations to develop Machine Learning Models in a very easy way, while having visibility, auditing, and reproducibility.
How we're going to make it easier and faster for today's practitioners to build machine learning applications. In short, we will:
- Keep product improvement faster and more focused.
- Make basic and common data science steps intuitive and automatic while hiding any unnecessary complexity.
- Expose major open-source tooling and initiatives.
- Make model development reviewable and continuous.
- Cover, in breadth, major aspects of the lifecycle by providing integrations with major tools.
When our short term phase is almost done, we'll reveal how we'll work toward:
- Improving and Increasing the depth of several aspects.
- Hiding more complexity, removing abstraction leakiness, and simplifying access.
- Exposing more information to upper management to increase visibility and reduce risk.
- Provide simpler interfaces for non-technical employees to leverage data for decision making.