Effective Machine Learning Teams: Best Practices for ML Practitioners
About this ebook
Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common traps in the ML world, so you can iterate and scale more quickly and reliably. You'll learn how to overcome friction and experience flow when delivering ML solutions.
You'll also learn how to:
- Write automated tests for ML systems, containerize development environments, and refactor problematic codebases
- Apply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutions
- Apply Lean delivery and product practices to improve your odds of building the right product for your users
- Identify suitable team structures and intra- and inter-team collaboration techniques to enable fast flow, reduce cognitive load, and scale ML within your organization
About the author
David Tan is a Senior ML Engineer at Thoughtworks. He has worked on multiple data and machine learning projects and applied time-tested software engineering practices to help teams iterate more quickly and reliably in the machine learning development lifecycle.
Ada Leung is a Senior Business Analyst at Thoughtworks. She has technology delivery experience across several industries and her experience includes breaking down complex problems in varying domains, including customer facing applications, scaling of ML solutions, and more recently, data strategy and delivery of data platforms. 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 "Dave" Colls 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.
