Room
Room F1 - Track 1: Talks
Audience
Everyone who wants to know basics of ML systems & how testers can contribute to test them.
Key-Learnings
- Importance of testing Machine learning enabled applications
- What contribution testers can make in data engineering teams
- How to create impact as testers in ML based projects
- Understanding building blocks of ML based system & advised practices to test them
Machine learning (ML) has become the epicenter of software in today’s era. Tremendous efforts are put into developing ML-based systems. But what about ensuring the quality of such applications? What role can testers play in this context?
Usually, testing takes a backseat here and testers struggle to find their role and contributions they could make. The perception remains that “It’s machine learning, it cannot be tested”. But is this really the case? If not, how can we break this myth? How can testers make their space in the ML world?
Join Shivani as she explores answers to such questions and many more by sharing her experience of testing ML-based applications without having any prior experience of it. She’ll reflect on her motivation, challenges faced, learnings and techniques to test such systems. Get to know how a tester's skill set like product knowledge, attention to details, thinking out of the box and customer focus can be game changer here. She’ll leave you with tips, tricks and ideas on how to create an impact on quality in such projects.