Thoughtworks Technology Podcast

Building at the intersection of machine learning and software engineering

May 2, 2024

Discover the challenges of bringing machine learning models into production and how new teams are bridging the gap between data science and software engineering. Learn about effective teamwork, rigorous testing, and collaboration between data scientists and product owners. Dive into the importance of trust, communication, and experimentation in building effective ML teams, and explore adapting ML techniques in the Gen R2 AI era. Enhance ML product development in modern digital organizations and focus on problem-solving and value delivery in machine learning products.

48:30

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Quick takeaways

Effective Machine Learning Teams book addresses technical challenges like testing and deployment.

Building successful ML products requires effective team collaboration and value understanding.

Deep dives

Developing Effective Machine Learning Teams

Building successful machine learning products requires effective team collaboration and understanding the value being delivered. The book emphasizes that every individual in an organization, regardless of background or experience, plays a crucial role in delivering great ML products.

Introduction

2min

Exploring the Intersection of Machine Learning and Software Engineering

10min

Effective Teams in Machine Learning Product Development

17min

Building Effective ML Teams Through Trust and Experimentation

12min

Adapting ML Techniques in the Gen R2 AI Era

4min

Enhancing ML Product Development in Modern Digital Organizations

2min

Focusing on Problem-solving and Value Delivery in Machine Learning Products

2min

Bringing machine learning models into production is challenging. This is why, as demand for machine learning capabilities in products and services increases, new kinds of teams and new ways of working are emerging to bridge the gap between data science and software engineering. Effective Machine Learning Teams — written by Thoughtworkers David Tan, Ada Leung and Dave Colls — was created to help practitioners get to grips with these challenges and master everything needed to deliver exceptional machine learning-backed products.

In this episode of the Technology Podcast, the authors join Scott Shaw and Ken Mugrage to discuss their book. They explain how it addresses current issues in the field, taking in everything from the technical challenges of testing and deployment to the cultural work of building teams that span different disciplines and areas of expertise.

Learn more about Effective Machine Learning Teams: https://www.thoughtworks.com/insights/books/effective-machine-learning-teams

Read a Q&A with the authors: https://www.thoughtworks.com/insights/blog/machine-learning-and-ai/author-q-and-a-effective-machine-learning-teams

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Thoughtworks Technology Podcast

Building at the intersection of machine learning and software engineering

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Developing Effective Machine Learning Teams

Reflection for Improvement

Focus on the Bigger Picture

Discovering Techniques for Success

Remember Everything You Learn from Podcasts