Building at the intersection of machine learning and software engineering

Building at the intersection of machine learning and software engineering

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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Episoder(100)

What does code mean in 2026?

What does code mean in 2026?

What is code? It might sound obvious, but if you scratch the surface it becomes more difficult to articulate precisely what we mean. AI is complicating the picture further and changing the relationshi...

25 Jun 40min

Database branching: Overcoming the bottlenecks of shared database environments

Database branching: Overcoming the bottlenecks of shared database environments

Database branching has, for a long time, been a troublesome piece in the modern developer workflow puzzle: a good idea in principle but in practice a slow and often expensive challenge. Get it right a...

11 Jun 39min

What is spec-driven development?

What is spec-driven development?

Semantic diffusion, combined with the pace of technology change, makes talking about AI-adjacent practices and techniques incredibly diffficult. There are few better examples of this issue than the te...

28 Mai 45min

What is harness engineering?

What is harness engineering?

'Harness engineering' is one of the most significant terms to emerge in software engineering in 2026. Broadly referring to the work done to control unpredictable AI agents and coding assistants, its u...

14 Mai 40min

Anthropic Mythos: Hype, reality and the actual security implications

Anthropic Mythos: Hype, reality and the actual security implications

Anthropic Mythos garnered significant attention when it was launched in mid-April 2026. Yet despite it apparently presenting an unprecedented threat to global software, you don't have to look to close...

30 Apr 48min

Key themes in Technology Radar Vol.34

Key themes in Technology Radar Vol.34

In April 2026 we published a new edition of the Thoughtworks Technology Radar — volume 34. Like many recent volumes, this one was dominated by AI. However, while editions over the last couple of years...

15 Apr 44min

How it feels to be a software engineer when AI is changing our relationship with code

How it feels to be a software engineer when AI is changing our relationship with code

There's been a lot of discussion and debate in recent months about exactly how software engineering will be reshaped by AI. While it remains to be seen what the discipline will look like once things q...

2 Apr 41min

Be brilliant at the basics: Inside Looking Glass 2026

Be brilliant at the basics: Inside Looking Glass 2026

The Thoughtworks 2026 Looking Glass report was published in January. Designed to provide business and technology leaders with the tools to better understand and navigate future trends, this edition pa...

19 Mar 46min

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