Dive Brief:

• On Monday, Amazon announced its 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. 
• More than 30 courses are self-service, self-paced and part of a new "AWS Certified Machine Learning - Specialty" certification. Starting with fundamentals and moving to real-world examples, students will learn how to apply ML to problems such as delivery route optimization and use AWS services such as DeepLens, SageMaker and Rekognition. 
• The programs target four key 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, according to the announcement. Courses are available free of charge, but students will have to pay for services used during labs and exams. 

Dive Insight:

The AI course announcement comes on the heels of a Coursera partnership for an AWS cloud fundamentals training program geared at new cloud developers and professionals. AWS already has a vast training program, but publicly available courses extend its reach to millions of new learners. 

Tying its AI 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 AI, certifications also serve as strong indicators of talent for employers — and perhaps some extra cherries on top of an already high salary. 

Amazon also joins its biggest cloud competitors in making internal AI training programs public. 

Google opened up its ML crash course in February, several weeks after launching an IT professional certificate program. Shortly thereafter, Microsoft kicked off an AI training program modeled after its internal courses in April. 

Programs and open knowledge bases offered by big tech companies serve as an alternative to traditional education programs which, often slow or unable to change, have slowed parts of the AI talent pipeline. 

The Massachusetts Institute of Technology's $1 billion College of Computing campus centered around AI and data science was a major step for AI programs in higher education. But it has also 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.