Waterfall cascading over ferny rock platforms

Stream of Teams

It was great to chat with Matthew Skelton about the intersection of Team Topologies and AI/ML product delivery – watch the recording. This was the subject of the final chapter of Effective Machine Learning Teams, in which we used the Team Topologies model to explore effectiveness at the level of “between teams”.

Building blocks of effective ML teams and organisations - shows a stack of factors at individual, within teams, and between teams levels. Factors are: trust, communication, diverse membership, purposeful progress, cognitive load, enterprise portfolio of work, organising for fast flow, role of leadership
Building bocks of effective organisations showing “between teams” factors (sketch artwork for Effective ML Teams)

We covered a lot of ground, including CD4ML, the building blocks above, and of course typical shapes, interaction modes and evolution of teams within an AI/ML product delivery organisation. We had a great conversation about the role and evolution of enabling teams in particular (and my slides).

A diagram showing the evolution of ML enabling teams

Check out the recording and read the Effective Machine Learning Teams book for the full story. Thanks David Tan for always bringing clear articulation of engineering challenges and Ada Leung for supporting from afar.

Livestream screen capture showing Matthew Skelton, David Tan and Dave Colls

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