Team Topologies in action: Effective structures for Machine Learning teams

Structuring teams for AI adoption is a challenge for every organization, but how do you structure your teams if you are working within Machine Learning and AI development? Is Team Topologies still effective in this context?

It’s no secret that Team Topologies is widely used to optimize the structures of software delivery teams. As the fundamental principles of Team Topologies don’t distinguish between human and AI team members or skills, team structures created from these organization design principles make it possible to quickly adopt AI tools and agents whilst scaling effectively. But what if your organization’s function is to develop Machine Learning (ML) models that underpin these AI tools? Can Team Topologies still create a blueprint for success?

Based on insights from the recent Stream of Teams episode Team Topologies for AI/ML with Team Topologies co-author Matthew Skelton and David Tan and Dave Colls, two of the authors of Effective Machine Learning Teams, we can examine how Team Topologies principles also provide robust approaches for structuring organizations developing for AI and ML.

Continuous Delivery for Machine Learning

Traditional organizations developing in the ML space often struggled with the divide between data science expertise and software engineering practices. Drawing from established software development patterns, successful ML organizations now recognize that engineering excellence forms the foundation of effective ML delivery. This manifests most clearly in adopting Continuous Delivery for Machine Learning (CD4ML), which enables rapid feedback cycles and systematic version control of model artifacts.

Designing teams for Machine Learning

Having proven its suitability to provide effective team structures for other industries, Team Topologies offers principles of organizational design that are highly congruent with successful ML development, identifying four fundamental team types, each serving a distinct purpose in ML organizations. 

Stream-aligned teams: These teams form the backbone of effective ML product delivery, owning end-to-end development while maintaining fast flow through well-defined boundaries. These teams typically begin with focused ML use cases, establishing patterns for continuous delivery that can scale across the organization.

Platform teams: As ML initiatives grow, platform teams emerge to provide essential infrastructure and tooling. These teams reduce cognitive load across the organization by managing shared data pipelines, computation resources, and ML governance frameworks. The success of platform teams in ML contexts relies heavily on their ability to create self-service capabilities that enable stream-aligned teams to maintain autonomy while benefiting from standardized tooling.

Enabling teams: These teams play a crucial role in ML organizations by bridging knowledge gaps between data science and engineering disciplines. Enabling teams often evolve from informal communities of practice, helping organizations navigate novel ML challenges while converting emerging patterns into reusable platform capabilities. Their effectiveness stems from their ability to facilitate knowledge transfer while focusing on practical implementation.

Complicated subsystem teams: In complex ML domains, these teams manage specialized expertise areas, providing sophisticated capabilities as services to stream-aligned teams. This pattern is particularly valuable when dealing with advanced model architectures or domain-specific ML applications requiring deep expertise.

Managing cognitive load for fast flow

The management of cognitive load is vital for any team and is particularly critical in ML organizations due to the inherent complexity of mathematical concepts, data processing requirements, and deployment considerations. Team Topologies provides a proven and effective approach for managing this complexity through clear team boundaries and interaction patterns, and we often find that organizations providing CD4ML have adopted team structures described by Team Topologies.

The three interaction modes identified in Team Topologies — Collaboration, X-as-a-Service, and Facilitating — gain new significance in ML contexts. Collaboration proves essential during initial ML implementation phases, while X-as-a-Service enables scaling of ML capabilities across organizations. The facilitating interaction mode supports the adoption of ML best practices and guides teams through engineering challenges.

Team Topologies implementation and evolution for AI

Organizations implementing Team Topologies for ML should begin with stream-aligned teams focused on well-defined use cases. As ML initiatives mature, platform capabilities evolve to support common infrastructure needs and standardize MLOps practices. Enabling teams deploy strategically to support novel implementations and guide technology transitions.

As AI continues to evolve, Team Topologies provides a flexible approach that adapts to emerging technologies while focusing on value delivery, enabling organizations to harness the power of AI/ML while avoiding the common pitfalls of organizational design and team cognitive overload.

Flow-centric team design

Success in this domain requires careful attention to team boundaries, interaction patterns, and cognitive load management while focusing on continuous delivery and value creation. Through the thoughtful application of Team Topologies principles, organizations can build sustainable, effective AI/ML development capabilities that drive business value while supporting team health and productivity.

As a Team Topologies Solutions Partner with a global network of experts in fast flow, Conflux helps leading organizations with insights and actionable strategies for digital transformation, helping your organization to evolve in line with the technologies it develops.

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