Jupyter trends in 2018

A few somewhat-unexpected things emerged in 2018:

• Large organizations began adopting Jupyter for analytics infrastructure, in a “leap frog” effect over commercial offerings. Many people hired out of universities already know how to write ML apps in Jupyter – and those without coding backgrounds can learn rapidly via Jupyter; why spend money re-training people to use proprietary frameworks which are less effective?
• An emerging trend of of hardware > software > process disrupts the past 15–20 years of software engineering practice. Jupyter helps “future proof” efforts during this chaos/evolution – and that dovetails quite well with cloud services.
• As organizations in enterprise, government, and universities roll out interactive computing at scale, organizational challenges arise: practices regarding collaboration, data privacy, ethics, security, compliance, etc. Jupyter addresses critical needs – which FAMGA hadn’t addressed – and these become particularly important in highly regulated environments.