Team Topology for Machine Learning

Nowadays, Machine Learning (ML) is all in rage worldwide. A lot of companies are adopting ML (or AI or Advanced Analytics or Data-Driven Decision Making) in their current business processes. In this organization, a lot of effort is going towards recruiting ML talents, forming teams, identifying the feature scope of the team. Like many tech organizations, these organizations are also producing monoliths applications, e.g., one platform that includes workflow orchestration, model management, feature management, ML application code, etc. When such an organization realizes that they have ten different teams with seven different architectures, they realize that it is neither scalable nor reasonable to be in such a situation.