#203 - Building Effective and Thriving Machine Learning Teams - David Tan & Dave Colls
Tech Lead Journal
Navigating Machine Learning Production Challenges
This chapter explores the hurdles faced by machine learning teams when transitioning projects from the prototype phase to successful production. It emphasizes the importance of aligning ML initiatives with business objectives, addressing skill gaps within teams, and employing effective collaboration strategies to boost project success.
Is your ML project stuck in POC hell and failing to deliver real value?
In this episode, I sit down with David Tan and Dave Colls, co-authors of the book “Effective Machine Learning Teams” to discuss the complexities of building and deploying ML models, building and managing effective ML teams, and ensuring successful ML projects.
Key topics discussed:
- Learn the key differences between ML engineering and traditional software engineering.
- Why so many ML projects fail to reach production or deliver real business value and how to overcome common challenges.
- Discover the ideal ML team composition and how to apply concepts like team topologies for effective collaboration.
- The importance of product thinking in ML projects and best practices for designing robust, valuable ML products.
- Essential engineering best practices for successful ML projects, including automated testing, continuous delivery, and MLOps practices.
Listen out for:
(02:35) Career Turning Points
(04:54) Writing "Effective Machine Learning Teams"
(08:29) ML Engineering vs Other Types of Engineering
(12:24) ML and LLM
(14:59) Why Many ML Projects Fail
(19:53) ML Success Modes
(23:32) Ideal ML Engineering Team Composition
(31:39) Building the Right ML Product
(39:23) ML Engineering Best Practices
(49:14) MLOps
(52:44) Make Good Easy
(53:56) 3 Tech Lead Wisdom
Tune in to learn how to build and deploy ML products that truly make a difference!
David Tan’s Bio
David Tan is a lead ML engineer with more than six 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.
Follow David Tan:
- LinkedIn – linkedin.com/in/davified
- X – @davified
- GitHub – https://github.com/davified
Dave Colls’ Bio
Dave loves building new things and helping others build too – products and services, people and teams – using technology, Data & AI.
His work building the Thoughtworks Data & AI practice shows how these elements combine: creating a new business, consulting to leaders and making a home for data people to grow. Currently, Dave is working at Nextdata, pushing the frontiers of how organisations work with data.
David thrives on navigating complex challenges and evolving environments. His extensive experience in large-scale digital transformations and deep tech R&D has honed his ability to align efforts and drive impactful results.
A dedicated mentor, David is passionate about guiding data professionals in their career growth. He actively shares his expertise as a speaker, blogger, and author of the book “Effective Machine Learning Teams.”
Follow Dave Colls:
- LinkedIn – linkedin.com/in/davidcolls
Links:
- 🎧 #198 - Better Software Faster: Measure & Improve Developer Productivity with DX Core 4 - Laura Tacho – https://techleadjournal.dev/episodes/198/
- 📚 Effective Machine Learning Teams – https://www.oreilly.com/library/view/effective-machine-learning/9781098144623/
- Free preview and overview – https://www.thoughtworks.com/content/dam/thoughtworks/documents/e-book/EffectiveMLTeams_preface.pdf
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