Exploring the Trade-Off: Centralized vs Embedded Models for ML
Scientists
Matteo Ruffini

Joos-Hendrik Boeseruffinim@amazon.de
Amazon Music ML
Berlin, Germany

jooshenb@amazon.de
Amazon Music ML
Berlin, Germany

DESCRIPTION OF THE TALK
Abstract
In the rapidly evolving landscape of machine learning (ML) management, organizations face a critical decision when it comes tostructuring their ML teams. This talk aims to compare and analyze
the merits and drawbacks of two prominent models: the centralized
ML science team model the and embedded ML scientists model. In
the centralized model, a Core ML team owns a central ML component, consulting the teams that own customer-facing features on
its usage. In the embedded model, scientists are directly assigned
to a feature-owning team, and own all the ML-related aspects ofthat team. We compare advantages and disadvantages of the twoapproaches, present case studies and discuss potential hybrid solutions that can combine the advantages of both approaches.

Potential Discussion Points• Definition and Key Characteristics: Understand the fundamental differences between the centralized and embedded
models, including team structure, reporting lines, and ownership of ML products.
• Deep Dive: the Centralized Model. Explore the benefits,
such as enabling long-term research, fostering a strong teamspirit, and developing scalable platform solutions. Discuss
the challenges, including slow adaptability to changing
business needs.
– Business Case study: Amazon Music Core ML team.
Describe high level team structure and the goal — consisting in delivering a centralized experimentation platform for ML. Describe benefits, success stories andchallenges.
• Deep Dive: the Embedded Model. Examine the advantages,
such as quick fulfillment of diverse business needs andthe opportunity for scientists to develop valuable productand customer obsession skills. Discuss the limitations, suchas potential limited long-term research opportunities anddifficulties in scaling solutions horizontally.
– Business Case study: Skyscanner Data Science team.
Describe high level team structure as a pure embedded
organization, success stories and issues.
• Hybrid Approaches: Explore the possibility of combining
elements from both models to strike a balance - including
having both a core team with additional embedded ML
scientists working directly with partner teams. Discussrisks — including an increased risk of churn for scientists
not working in a central team — and potential mechanisms
for seamless transition between centralized and embeddedroles, mitigating frustration and friction among scientists.

Relevance to Workshop
As machine learning applications become increasingly prevalent
in various industries, effective management of ML teams, products, and projects becomes crucial for achieving business impact.
Understanding the advantages and disadvantages of different organizational models will equip participants with valuable insights
and methodologies to optimize their ML management practices.
By delving into the complexities of ML management structures,
this talk aims to provide attendees with a comprehensive understanding of the trade-offs involved in adopting a centralized orembedded model for ML scientists. The insights shared and discussions held will contribute to the development of management
methods and best practices in the field, empowering participants tomake informed decisions and drive successful ML initiatives within
their organizations.

INFORMATION ABOUT THE PRESENTER
A short bio of the main presenter
Matteo Ruffini is an Applied Science Manager at Amazon Music,
where he leads a team of Machine Learning scientists working onMusic Personalisation. He holds a PhD in Artificial Intelligencefrom Barcelona Tech University (UPC) and his publication record
includes papers presented at NeurIPS, The Web Conference, ML
Journal and ECML. Before joining Amazon, Matteo held the position of Data Science Manager at Skyscanner, where he focusedon personalization strategies and spearheaded experimentation
protocols.

A brief company or project portrait
Amazon Music is a music streaming service offered by Amazon. It
provides users with access to a catalog of 100 million songs andthousands of expert-programmed playlists and stations, albums,and podcasts. With a focus on personalization, Amazon Musicutilizes machine learning and data-driven algorithms to tailor recommendations and create customized audio experiences for its
customers.