11 May 15:00 — 15:45
About the session
We have come a long way in the world of machine learning, with complex models being built that are capable of doing a lot of great things.
The systems that can make these possible are complex by nature, and so are the teams that build them, typically comprising data engineers, data scientists and IT engineers.
The projects in the field of machine learning and artificial intelligence are complex and often experimental by nature, making agile practices difficult to implement. In this session, we will go through an experience of implementing agile practices in the machine learning field.
This session will include:
- A light introduction to machine learning and how it differs from other software engineering teams.
- Overview of the organisation’s agile adoption journey and the stages the teams went through to reach a system which works for them currently (and might well change with the experience gained).
- The session will be part case study, sharing a real failure experience and implementing the learning from it to reach the next stage in the transformation journey.
- The talk will also cover metrics and how they might be different for teams in the machine learning industry.
- Agile implementation in the machine learning industry
- Metrics that can help researchers/data scientists in an agile environment
- Frameworks that can work well with software engineers, researchers, and data scientists.
Agile Implementation, Machine Learning, Metrics, Agile Transformation, Retrospection, Cultural Shift, Agile Mindset, People