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Tech Lead Journal

#203 - Building Effective and Thriving Machine Learning Teams - David Tan & Dave Colls

Jan 27, 2025
David Tan, a lead ML engineer passionate about effective engineering, and Dave Colls, a digital transformation expert, delve into building successful machine learning teams. They discuss the critical differences between ML and traditional software engineering. Learn why many ML projects struggle to deliver value and discover strategies for overcoming these challenges. The duo highlights the importance of team composition and product thinking, sharing essential best practices like automated testing and continuous delivery to ensure robust ML solutions.
58:41

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Quick takeaways

  • Many machine learning projects fail due to 'POC hell', emphasizing the need for structured methodologies to ensure business value delivery.
  • Cross-functional collaboration is essential in ML product development to align technical and business teams, enhancing the delivery of valuable solutions.

Deep dives

Common Failure Modes in ML Projects

Many machine learning projects encounter common failure modes that hinder their success, one of which is often referred to as 'POC hell.' This term describes situations where teams repeatedly create proof-of-concept models without progressing to production due to a lack of business commitment or appetite for risk. Another frequent issue is the difficulty in evolving initial models, which may start off functional but quickly become difficult to modify or test, leading to a tangled and unscalable codebase. Understanding these pitfalls is crucial as they illustrate the need for structured methodologies to ensure that machine learning initiatives effectively solve real business problems.

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