Blackbox testing or a dozen machine learning projects experience
  • 40 min

What is the difference between ML-project and any other for a tester? How to test the subject that is not described by the classic concept of the expected / actual result? What exactly is a reasonable division of areas of responsibilities or, in other words, how can a tester not slide into Data Science? 

The talk focuses on these and many other issues. Through the prism of practical experience of a dozen machine learning projects, the author analyses the problems he faced across at the stages of the approach to testing formation.

Comments ({{Comments.length}} )
  • {{comment.AuthorFullName}}
    {{comment.AuthorInfo}}
    {{ comment.DateCreated | date: 'dd.MM.yyyy' }}

Chat with us, we are online!