Amazon Web Services (AWS) wants to help companies train its staff through the ML Embark program, which it introduced at re:Invent in Las Vegas. ML Embark is an employee training service provided by AWS’ machine learning experts.

Michelle Lee, head of the AWS Machine Learning Solutions Lab, wrote in a blog post that ML Embark uses a number of lessons learned by parent company Amazon.com while putting together internal AI teams.

One of these lessons is the need to formulate a clear project objective for employees. ML Embark training programs therefore begin with an exercise in which technical and non-technical personnel from the participating company must work together to identify a business problem that they can solve with machine learning.

AWS also organises a number of on-site training courses to equip employees with the skills they need to carry out their specific idea. The training sessions use a “curriculum modeled after Amazon’s Machine Learning University, which has been refined over the last several years to help Amazon’s own developers become proficient in machine learning,” wrote Michelle Lee, head of the AWS Machine Learning Solutions Lab. The company already opened the Machine Learning University for companies last year, with about 30 available courses.

DeepRacer

ML Embark training courses are concluded with a number of assignments designed to give workers the opportunity to use their new AI skills. There is a proof-of-concept development project in which the participants build a machine learning application. There is also a DeepRacer competition; DeepRacer is a stand-alone, remotely controllable miniature car to help developers learn more about reinforcement learning, an emerging form of machine learning.

ML Embark uses this DeepRacer event as a way to expand interest in AI to the rest of the organisation, beyond the employees who receive direct training. The event helps “expose a broader group of employees to machine learning with friendly competition and hands-on experience through racing,” Lee stated.