AWS announces the Machine Learning Embark program to help customers train their workforce in machine learning
Today at AWS re:Invent 2019, I’m excited to announce the AWS Machine Learning (ML) Embark program to help companies transform their development teams into machine learning practitioners. AWS ML Embark is based on Amazon’s own experience scaling the use of machine learning inside its own operations as well as the lessons learned through thousands of successful customer implementations. Elements of the program include guided instruction from AWS machine learning experts, a discovery workshop, hand-selected curriculum from the Machine Learning University, an AWS DeepRacer event, and co-development of a machine learning proof of concept at the culmination of the program.
Customers I talk to are eager to get started implementing machine learning in their organizations, but it can be difficult to know where to begin. And, once started, it can be challenging to gain meaningful adoption across the organization. More often, customers are not asking “why” machine learning, but “how.” It’s a cultural shift as much as a technical one. Success involves inspiring and motivating teams to get interested in machine learning, identifying the most impactful projects to tackle, and developing a workforce with the right skills. And, teams new to machine learning need guidance and expertise from more seasoned data scientists who are in short supply. As a result, organizations can often feel like turning the corner on machine learning adoption happens at a glacial pace.
The AWS ML Embark program is designed to help these customers overcome some common challenges in the machine learning journey. To kick off the program, participants will pair their business and technical staff with AWS machine learning experts to join a discovery day workshop to identify a business problem well suited for machine learning. Through this exercise, AWS machine learning experts will help the group work backwards from a problem and align on where machine learning can have meaningful impact.
Next, this cross-functional group will participate in instructor-led, on-site trainings with 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. Participants will benefit from hand-selected coursework focused on practical application relevant to their business use cases. At the completion of the training, the AWS ML Embark program offers the option to continue education online and take the AWS Certified Machine Learning – Specialty certification exam to validate their skills.
AWS ML Embark also includes a corporate AWS DeepRacer event to expose a broader group of employees to machine learning with friendly competition and hands-on experience through racing fully autonomous 1/18th scale race cars using reinforcement learning.
Finally, experts from the Amazon ML Solutions Lab mentor participants through the ideation, development, and launch of a proof of concept based on a use case identified in the discovery day workshop. Through the process, the team will gain insight into best practices, ways to avoid costly mistakes, and knowledge based on the overall experience of working with experts who have completed hundreds of machine learning implementations.
At the conclusion of the program, a customer is well prepared to begin scaling newly obtained machine learning capabilities throughout their organization to take on additional machine learning projects and solve new challenges across their business. We’re excited to help customers begin their machine learning journey and can’t wait to see what they’ll do after graduation. Nominations for the program are now being accepted.
About the Author
Michelle Lee is vice president of the Machine Learning Solutions Lab at AWS.