This paper describes an effective agile process for teaching technical subjects in the workplace. For example, data security, machine learning, cloud computing are technical subjects.
With the advent of Big Data and with today’s breakneck speed of technical innovation it is more important than ever to provide the technical workforce with continuous education.
But many questions need answers: how frequently should a full-time employee be diverted from work due to their education? What budget should be assigned to the continuous education of technical employees? How can the teaching material be always kept up to date and relevant to the company?
As we were tasked to teach machine learning to a large portion of the software engineering workforce, we had to ask ourselves these questions and more, and after a few iterations we reached our current process which seems to “just work”: no extra budget and an average student’s NPS (net promoter score) nearing 100%. The process uses in-house technical experts to design and teach short lessons, and it uses past graduates to act as mentors for new students. In addition, the process has the originally unplanned benefit of favoring networking among employees from different parts of the company.
Encouraged by our successes we recently applied the same teaching process to new technical domains: Data Science and Full Stack DevOps, again, it seems to just work. No extra budget and an average student’s NPS in the 90s.
In this paper we answer the above questions and more, we detail our teaching process, and we share some quantified results.