- Amazon's internal machine learning courses for engineers are now public on the Amazon Web Services platform, according to a blog post by Matt Wood, general manager of artificial intelligence at AWS.
- The 30-plus courses are self-service, self-paced and part of a new AWS certification for machine learning. Starting with fundamentals and moving to real-world examples, students will learn how to apply machine learning to issues such as delivery route optimization and use AWS services such as DeepLens, SageMaker and Rekognition.
- The program targets four groups — data scientists, business professionals, data platform engineers and developers — with a variety of courses, videos and labs that span more than 45 hours of instruction. The courses are free, but students have to pay for services used during labs and exams.
Amazon's announcement comes on the heels of a Coursera partnership for an AWS cloud fundamentals training program geared at new cloud developers and professionals. AWS has a vast training program, but publicly available courses could extend its reach to millions of learners.
Bringing its machine learning courses into a formal certificate program provides a strong incentive for developers. AWS certificates account for two of the top three highest-paying, non-security IT certifications. In a field as in-demand as artificial intelligence (AI), which includes machine learning, certifications also serve as indicators of pre-vetted talent for employers.
Amazon joins its biggest cloud competitors in making internal AI training programs public. Google opened up its machine learning crash course in February, several weeks after launching an IT professional certificate program. Shortly thereafter, in April, Microsoft kicked off an AI training program modeled after its internal courses.
Programs and open knowledge bases offered by major tech employers serve as an alternative to traditional education programs — and colleges are beginning to take notice. Companies such as Amazon, Apple, Facebook and Google have created curriculum for colleges and are working with them to implement it.
Other institutions are making bigger moves. Last month, the University of California, Berkeley, announced it would create an interdisciplinary data science division in response to growing interest in the field. And in October, the Massachusetts Institute of Technology revealed plans for a $1 billion College of Computing and an overhaul of how it teaches AI.
Such moves also have sparked dialogue among educators and professionals about whether to fold new technologies such as AI into traditional computing programs or to spin them off as distinct units, as well as how to extend these opportunities beyond top-tier STEM institutions.