Technical barriers to getting Machine Learning models in production are well understood and increasingly well-solved, so “number of models in production” is no longer a useful measure of your team’s value. The next frontier is to connect those models directly to the levers that generate business value.

The challenge is that data leaders pay the costs of developing and maintaining ML models, but the benefits they generate are decided and realised by others: IT owners, P&L holders, digital transformation leads. In other words, having cleared one set of obstacles the goalposts move; the goal is no longer POCs, pilots or even a specific number of models in production, for the glory of data experts, but tangible economic benefits for the benefit of others.

In this event, moderated by Dataiku your peers will discuss:
– How they have kept the business focused on tangible benefits while keeping data scientists occupied with challenging work
– What pitfalls they have encountered in changing expectations from output driven by data to outcomes owned by others
– Practical strategies to move the goalposts yourself, before anyone else does

Speaker

Shaun McGirr is a data leader with experience across official statistics, academia, consulting, and now data science in a large automotive services company. He recently achieved minor stardom in a documentary Data Science Pioneers, coining the phrase “things that happen 35% of the time, happen ALL the time” to explain why quite likely outcomes are often dismissed out of hand. Shaun believes the toughest part of doing data well is finding the right questions and ensuring the answers will actually, push a lever to change the world, a theme developed further in his podcast Half Stack Data Science.