CMS Partner Tech Talk
IKASI -- Application of Machine Learning Today: Tradeoffs in objectives, interpretability, and operationalization
The world of Data Science has gone through radical change over the last decade. As Business Intelligence tools and application of Machine Learning have improved and hiring data scientists has become common practice, business analytics has grown into a sophisticated effort to find "actionable insights". In other words, what factors drive user behavior and how can companies use them to proactively engage with customers or create policies that drive desired outcomes?
Maturation has opened up interesting questions in computing methodologies: what is the right objective (e.g. ROC) to create predictions on? How important is interpretability? How can results be operationalized?
We will go through some of the tradeoffs our clients and practitioners commonly face in application to their business problems (comparing experiences ranging from Facebook and Google to Small Startups). We'll also go through a real financial project derived from a case study of one of our clients.
Contact: Claire Ralph firstname.lastname@example.org