AI is Our Generations’ Race to the Moon (Part 2)

(First part of this article was published on Oct 27)

Same will happen in Machine Learning Operations (MLOps), where in the beginning we will see a lot of proprietary, home-grown, platforms arise, only to be replaced by something more universal, and much better, years from now.

Key to AI Success is in Wide Availability

Our vision is to make AI available to the masses, to make AI solutions available on 24/7 basis, over the Cloud, at a lower cost, and greater efficiency, while being user-friendly, customizable, affordable, easy to set up, and easy to integrate with existing software platforms. When that is achieved, machine learning models will be in production within hours, or even minutes, from when they are trained, versus months it takes now, if they ever do because over 90% of models industry-wide fail in production.

However, as improbable as this seems right now, we can see this in the future, as we’ve seen it happen before. Just like the improvements in the internet drove adoption of the SaaS model, so will improvements in both internet speed, edge computing, and computational capabilities in the cloud, driven by cutting-edge technologies like software-defined cloud chips, also known as reconfigurable AI, drive en masse adoption of AI. This capability to personalize infrastructure will enable rapid development and deployment of new AI solutions.

Traditional Training/Lerning Methods are not Effective in AI

We need more people trained in AI and machine learning. There is an enormous shortage of people qualified to work on AI and machine learning projects, and technologies. We believe that by connecting experts with those working, or wanting to work, on AI and machine learning projects, will create the fastest and the most meaningful transfer of knowledge possible. AI knowledge changes so rapidly that one cannot acquire practical expertise required for moving a model from training into production by learning it online, or in school, or in any traditional learning environment.

387 Labs brings top AI and machine learning experts to guide and mentor data and machine learning engineers while they work on projects in real-time. We also organize training programs available to anyone, where we bring together future AI and machine learning engineers, and top experts who then guide and train these engineers while they work on real-world AI and machine learning projects. That accomplishes several important goals: trains more engineers, enables companies to hire trained AI and machine learning engineers after the program, and solves backlog of AI and machine learning projects during the program.

The future of AI is clear, but the path to achieving that future is still being paved. We want to be a key contributor to paving the path into the AI future.

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