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Effective Machine Learning Teams
Effective Machine Learning Teams

Effective Machine
Learning  Teams

Best practices for ML practitioners
Book cover of Effective Machine Learning Teams: Best Practices for ML Practitioners by David Tan and Ada Leung. The image on the cover is of three birds on a branch.

Many organizations start their machine learning (ML) journey with high hopes, but the lived experiences of many ML practitioners tell us that the journey of delivering ML solutions is riddled with traps, quicksand, and even seemingly insurmountable barriers. When we peel back the hype and the glamorous claims of being the sexiest job of the 21st century, we often see ML practitioners bogged down by burdensome manual work, team silos, and complex, brittle and unwieldy solutions.


This hinders the delivery of value to customers and also frustrates an organization’s investments and ambitions in AI.


To address these challenges, Effective Machine Learning Teams draws on the authors’ experience across real-world data and ML projects to distill the proven techniques that will help your teams reduce friction, shorten feedback cycles and deliver value reliably when building ML solutions.


Order your copy of Effective Machine Learning Teams online today or access it via the O'Reilly website. For a sneak peek of the book, download the preface.

About the authors

Ada Leung

Senior Business Analyst, APAC, Thoughtworks

Ada has 5+ years of technology delivery experience across several industries. Her experience includes breaking down complex problems in varying domains, including customer facing applications, scaling of ML solutions, data strategy and experimentation, and more recently, delivery of data platforms and large scale data migrations. She has been part of exemplar cross-functional delivery teams, both in-person and remotely, and is an advocate of cultivation as a way to build high performing teams.

David Colls

Director, Data & AI Practice, APAC, Thoughtworks

David is a technology leader with broad experience helping software and data teams deliver great results. David's technical background is in engineering design, simulation, optimization and large-scale data-processing software. At Thoughtworks, he has led numerous agile and lean transformation projects, and most recently he established the Data and AI practice in Australia. In his practice leadership role, he develops new ML services, consults on ML strategy and provides leadership to the delivery of ML initiatives.

David Tan

Lead ML Engineer, APAC, Thoughtworks

David is a lead ML engineer with 6+ years of experience in practicing Lean engineering in the field of data and AI across various sectors such as real estate, government services, and retail. David is passionate about engineering effectiveness and knowledge sharing, and has also spoken at several conferences on how teams can adopt Lean and continuous delivery practices to effectively and responsibly deliver AI-powered products across diverse industries.