Moving from Software Engineering to Machine Learning

Matthew Beleck on November 11, 2020

Matthew Beleck from The Working Group (TWG) discusses the differences between traditional software engineering and working on projects that require data engineering and machine learning. Data projects often present unique challenges to engineers, as data quality and availability can impact your ability to complete a product build. Furthermore, ML and data projects can fail for reasons beyond the data itself, such as the difficulty of building an effective model or lack of compute resources. This has many impacts on projects and engineering processes: potential tension with agile or scrum approaches, the need for planning and managing data pipelines, and more.

Matthew Beleck is a Technical Lead at TWG, and has extensive experience architecting, building, and launching technical products, especially ones depending on large enterprise data set.