Navigating the Decision: Does Your team Need a Dedicated Machine Learning Team?
Are you wondering whether having your own Machine Learning (ML) team is the right move for your organization? Let’s delve into this question over the next few minutes.
Let’s begin by assessing your team’s needs. Is ML the core capability of your team? Consider products like Google’s Machine Translation, where ML engineers are pivotal contributors. If ML forms the cornerstone of your product, embedding ML engineers within your team is essential.
Alternatively, if ML plays a supplementary role, contributing less than 20% to your team’s success, or if it’s solely used for data analysis and insights generation, having ML engineers within your team may not be necessary. Take, for instance, Amazon’s Checkout page team, which doesn’t require ML for conversion optimization.
Does ML contribute significantly, say more than 30%, to your team’s success? Google Maps, for instance, relies heavily on grunt work to build its foundational map and uses ML for its applications like navigation and recommendations. In such cases, the decision on where ML talent should sit requires deeper consideration.
For scenarios where ML significantly impacts your team’s success, we need to consider both necessary and sufficient conditions…
