FinML — Finding Value in Machine Learning

In this week’s meetup Byron walked us through AccelerateML, a framework he has used successfully to guide the discovery phase of Machine learning projects.

Key takeaways from the talk

• Discovery may be only one part of the ML cycle but it is likely the most under-valued at the moment, given the focus on cutting edge models and shiny new tools.
• A thoughtful discovery phase is a foundation building process where cross-functional involvement (e.g. buy-in and participation from stakeholders that might be the biggest blockers) is paramount to the future success of a project.
• A time-boxed development phase coupled with a business metric driving deployment help to provide guard rails for ML projects.
• Best practices combine design thinking, lean patterns and agile ways of working. This could now be extended to the use of tools like Miro given the WFH world we live in.
• Frameworks such as Accelerate ML are applicable not only to ML but also other high risk tasks (i.e. lots of unknowns at the beginning) with many competitive permutations (i.e. which problem do we focus on first).
• Structure to ML workflows is needed to de-risk and avoid rabbit holes (i.e. endless research that leads to wasted effort) and secure buy-in from non-technical (as well as other non-ML technical) stakeholders.

Watch the full session here:

FinML - Finding Value in Machine Learning

Presentation
Original blog about discovering value

On FinML

FinML is a meetup group dedicated to applications of Machine Learning in finance. We are a group that is dedicated to discuss the economic and statistical concepts behind running Machine Learning in the real world. We strongly believe in discourse, which is why our sessions are 30 min presentation and 30 min open discussion. Sign up here to be invited to all of our meetups and contact myself in case you are interested in speaking at an event!