As my experience ranges from consulting through in-house to freelancing in ML / AI, I will give a multi-level perspective on questions and trade-offs a head / director of data science encounters in every-day work, including:
- vendor selection vs in-house development
- model selection: top-down vs bottom-up approach
- paths to maximizing value: focus on algorithms vs feature enginering,
- right moment to stop development
- balancing quality and business-driven output.
Former Head/Director of Data Science
03:20PM - Day 1