Increase reliability in data science and machine learning projects with CircleCI

Noah Gift

Abstract

Data science is no longer a niche topic at companies. Everyone from the CEO to the intern knows about how valuable it is to take a scientific approach to dealing with data. Consequently, many people not directly in software engineering fields are starting to write more code, often in the form of interactive notebooks, such as Jupyter. Software engineers have typically been huge advocates of build systems, static analysis of code, and generating repeatable processes to enforce quality. What about business people who are writing code Jupyter Notebooks? What processes can they use to make their data science, machine learning, and AI code more reliable?