Today, we’re pleased to launch Machine Learning Essentials for Business and Technical Decision Makersa series of three free, on-demand, digital-training courses from AWS Training and Certification. These courses are intended to empower business leaders and technical decision makers with the foundational knowledge needed to begin shaping a machine learning (ML) strategy for their organization, even if they have no prior ML experience. Each 30-minute course includes real-world examples from Amazon’s 20+ years of experience scaling ML within its own operations as well as lessons learned through countless successful customer implementations. These new courses are based on content delivered through the AWS Machine Learning Embark program, an exclusive, hands-on, ML accelerator that brings together executives and technologists at an organization to solve business problems with ML via a holistic learning experience. After completing the three courses, business leaders and technical decision makers will be better able to assess their organization’s readiness, identify areas of the business where ML will be the most impactful, and identify concrete next steps.

Last year, Amazon announced that we’re committed to helping 29 million individuals around the world grow their tech skills with free cloud computing skills training by 2025. The new Machine Learning Essentials for Business and Technical Decision Makers series presents one more step in this direction, with three courses:

  • Machine Learning: The Art of the Possible is the first course in the series. Using clear language and specific examples, this course helps you understand the fundamentals of ML, common use cases, and even potential challenges.
  • Planning a Machine Learning Project – the second course – breaks down how you can help your organization plan for an ML project. Starting with the process of assessing whether ML is the right fit for your goals and progressing through the key questions you need to ask during deployment, this course helps you understand important issues, such as data readiness, project timelines, and deployment.
  • Building a Machine Learning Ready Organization – the final course- offers insights into how to prepare your organization to successfully implement ML, from data-strategy evaluation, to culture, to starting an ML pilot, and more.

Democratizing access to free ML training

ML has the potential to transform nearly every industry, but most organizations struggle to adopt and implement ML at scale. Recent Gartner research shows that only 53% of ML projects make it from prototype to production. The most common barriers we see today are business and culture related. For instance, organizations often struggle to identify the right use cases to start their ML journey; this is often exacerbated by a shortage of skilled talent to execute on an organization’s ML ambitions. In fact, as an additional Gartner study shows, “skills of staff” is the number one challenge or barrier to the adoption of artificial intelligence (AI) and ML. Business leaders play a critical role in addressing these challenges by driving a culture of continuous learning and innovation; however, many lack the resources to develop their own knowledge of ML and its use cases.

With the new Machine Learning Essentials for Business and Technical Decision Makers course, we’re making a portion of the AWS Machine Learning Embark curriculum available globally as free, self-paced, digital-training courses.

The AWS Machine Learning Embark program has already helped many organizations harness the power of ML at scale. For example, the Met Office (the UK’s national weather service) is a great example of how organizations can accelerate their team’s ML knowledge using the program. As a research- and science-based organization, the Met Office develops custom weather-forecasting and climate-projection models that rely on very large observational data sets that are constantly being updated. As one of its many data-driven challenges, the Met Office was looking to develop an approach using ML to investigate how the Earth’s biosphere could alter in response to climate change. The Met Office partnered with the Amazon ML Solutions Lab through the AWS Machine Learning Embark program to explore novel approaches to solving this. “We were excited to work with colleagues from the AWS ML Solutions Lab as part of the Embark program,” said Professor Albert Klein-Tank, head of the Met Office’s Hadley Centre for Climate Science and Services. “They provided technical skills and experience that enabled us to explore a complex categorization problem that offers improved insight into how Earth’s biosphere could be affected by climate change. Our climate models generate huge volumes of data, and the ability to extract added value from it is essential for the provision of advice to our government and commercial stakeholders. This demonstration of the application of machine learning techniques to research projects has supported the further development of these skills across the Met Office.”

In addition to giving access to ML Embark content through the Machine Learning Essentials for Business and Technical Decision Makers, we’re also expanding the availability of the full ML Embark program through key strategic AWS Partners, including Slalom Consulting. We’re excited to jointly offer this exclusive program to all enterprise customers looking to jump-start their ML journey.

We invite you to expand your ML knowledge and help lead your organization to innovate with ML. Learn more and get started today.


About the Author

Michelle K. Lee is vice president of the Machine Learning Solutions Lab at AWS.