Reviewing Amazon’s Machine Learning University – Is it Worth All of the Hype? Reviewing Amazon’s Machine Learning University – Is it Worth All of the Hype?
As an educator in the field of data science, I’m always interested in new learning resources for machine learning. The industry... Reviewing Amazon’s Machine Learning University – Is it Worth All of the Hype?

As an educator in the field of data science, I’m always interested in new learning resources for machine learning. The industry needs a new crop of data scientists to fill the rising demand. This is why I was pleased to learn of the recent announcement of free access to Amazon’s Machine Learning University (previously limited to Amazon employees only). The same machine learning courses used to train Amazon employees are now available to all data scientists and data engineers through AWS.

[Related Article: How to Learn Data Science for Free]

For 20+ years, the company has been using machine learning across the Amazon enterprise. The company has already led thousands of data scientists along the path of building more intelligent applications through machine learning with services such as Amazon SageMaker (general machine learning), Amazon Rekognition (image and video analysis), Amazon Comprehend (NLP), Amazon Transcribe (automatic speech recognition), Amazon Polly (text to speech using deep learning), Amazon Translate (neural machine translation), and Amazon Lex (chatbots).

The offering includes more than 30 self-service, self-paced digital courses with more than 45 hours of courses, videos, and labs for four key groups of learners: business decision makers, developers, data scientists, and data platform engineers. Each course starts with core elements, and builds on those fundamentals through real-world examples and labs, allowing new data scientists to explore machine learning through actual problems Amazon had to solve. Coursework helps consolidate best practices, and demonstrates how to get started on the AWS machine learning services mentioned above. Here is a brief description for each learning path:

  • Business Decision Maker – this path is designed for business individuals and team leaders who are interested in leveraging machine learning, artificial intelligence, and deep learning to identify growth strategies for their organization and how these technologies will deliver business value as part of a larger digital transformation underway in your enterprise.
  • Developer – this path is designed for builders and software developers who want to use machine learning and artificial intelligence to better collaborate with data scientists in order to integrate machine learning technologies. 
  • Data Scientist – this path is designed for learners skilled in math, statistics, and analytics who want to become machine learning subject matter experts within their organization by learning how machine learning frameworks and analysis tools can apply to your work and improve collaboration.
  • Data Platform Engineer – this path is designed to prepare data platform engineers for how machine learning will change data ingestion, system requirements and performance, and the customer experience for the systems, services, and applications they support.

Here is the course path for data scientists:

Reviewing Amazon's Machine Learning University

The suite of courses offered for data scientists is robust. I especially like that “Math for Machine Learning” is included for a well-rounded embrace of the subject.

For business professionals, the courses serve to demystify artificial intelligence, machine learning, and deep learning, as well as provide an understanding of the terminology and how to use AWS machine-learning services to build models and add intelligence to applications.

To help data scientists demonstrate their new knowledge (and to help potential employers hire more efficiently), the company also announced the new “AWS Certified Machine Learning – Specialty” certification which uses an exam format to test to and validate a learner’s knowledge about machine learning, such as which approach is optimal for specific problem domain areas, and which AWS services to use for different applications of machine learning. There is a distinct learning path specifically for individuals preparing to take the AWS Certified Machine Learning – Specialty exam.

[Related Article: Google Makes Free and Accessible AI Education for Everyone]


If you find yourself on a career path that includes machine learning on the AWS cloud platform, then I’d take a serious look at the newly offered free educational resources from Amazon Machine Learning University. AWS is the platform of choice for many companies, from large enterprises to small start-ups, so if you’re tooling-up or just retooling, this would be a great place to start and gain some valuable experience. Amazon’s Machine Learning University definitely lives up to the hype.

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.