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

Can AI improve the quality of our code? A recent white paper published by code analysis company CodeScene — "Refactoring vs. Refuctoring: Advancing the state of AI-automated code improvements" — highlighted some significant challenges: in tests, AI solutions only delivered functionally correct refactorings 37% of the time. However, there are nevertheless opportunities. The white paper suggests it might be possible to dramatically boost the success rate of AI refactoring to 90%.

In this episode of the Technology Podcast, Adam Tornhill, CTO and Founder of CodeScene, joins Thoughtworks' Rebecca Parsons (CTO Emerita), Birgitta Böckeler (Global Lead for AI-assisted software delivery) and Martin Fowler (Chief Scientist and author of the influential Refactoring book) to discuss all things AI and code. From refactoring and code quality to the benefits and limitations of coding assistants, this is an essential conversation for anyone that wants to understand how AI is going to shape the way we build software.

Read CodeScene's Refactoring vs. Refuctoring white paper, which explores AI's role in improving code: https://codescene.com/hubfs/whitepapers/Refactoring-vs-Refuctoring-Advancing-the-state-of-AI-automated-code-improvements.pdf

Read CodeScene's Code Red white paper to learn how code quality impacts time-to-market and product experience: https://codescene.com/hubfs/web_docs/Business-impact-of-code-quality.pdf

CodeScene's new automated refactoring tool is now in beta. Learn more: https://codescene.com/campaigns/ai

Listen to our podcast discussion about AI-assisted coding from November 2023: https://www.thoughtworks.com/insights/podcasts/technology-podcasts/ai-assisted-coding-experiences-perspectives

If you've ever wondered how to measure your cloud carbon footprint, you can — thanks to a tool that's called, somewhat unsurprisingly, Cloud Carbon Footprint. Launched in March 2021 by Thoughtworks as an open-source project, it allows users to monitor and measure carbon emissions and energy use from cloud services.

On this episode of the Technology Podcast, senior software engineers Cameron Casher and Arik Smith join Alexey Boas and Ken Mugrage to talk about Cloud Carbon Footprint in depth. They explain why CCF is different from the measurement tools offered by established cloud vendors, how it actually works and how you can get started with it yourself.

CCF on GitHub: https://github.com/cloud-carbon-footprint

Learn more: https://www.cloudcarbonfootprint.org/

Looking Glass isn't like most other technology trend reports. It doesn't just tell you what deserves your attention, it's designed to help you use it to focus on what really matters to you. Published once a year, Thoughtworks intends it to be a tool that helps readers make sense of the emerging technologies that are going to shape the industry in the months and years to come.

In this episode of the Technology Podcast, lead Looking Glass contributors Rebecca Parsons and Ken Mugrage trade hosting duties for the guest seats, as they talk to Neal Ford about the most recent edition of the Looking Glass (published in January 2024). They explain what the Looking Glass is and outline some of the key 'lenses' that act as a framework readers can use to monitor and evaluate what's on the horizon.

Covering everything from AI to augmented reality, this conversation offers a new perspective on emerging technology to help prepare you for 2024.

Explore Looking Glass 2024: https://www.thoughtworks.com/insights/looking-glass

A few years ago, Thoughtworker and (prolific) author Neal Ford published Fundamentals of Software Architecture with Mark Richards. They're now back with another book on software architecture — written with co-author Raju Gandhi — which offers readers a very different learning experience. Described as a combination of technical book and graphic novel, Head First Software Architecture dispenses with dense prose to present and explain software architecture concepts and ideas in some highly innovative and novel ways.

In this episode of the Technology Podcast, the authors — alongside their editor, Sarah Grey — join Rebecca Parsons to discuss their new book. They explain the thinking behind the approach, how it diverges from Fundamentals of Software Architecture and detail some of the challenges of writing in a new format.

Whether you're interested in getting started with software architecture or simply curious about technical communication and learning, listen to find out more.

Learn more about Head First Software Architecture: https://www.oreilly.com/library/view/head-first-software/9781098134341/

Distributed systems are ubiquitous yet complex. They can be particularly demanding for software developers and architects tasked with dealing with the sometimes unpredictable nature of the interactions between their various parts.

That's why Thoughtworker Unmesh Joshi wrote Patterns of Distributed Systems. Published at the end of 2023, the book explores a number of patterns that characterize distributed systems, and uses them to not only help readers better understand how such systems work but also to solve problems and challenges that often arise.

On this episode of the Technology Podcast, Unmesh joins hosts Scott Shaw and Rebecca Parsons to talk about his book, explaining where the idea came from, how he put it together and why it's important to get beneath neat abstractions to really get to grips with the inner workings of distributed systems.

Learn more about Patterns of Distributed Systems: https://www.pearson.com/subject-catalog/p/patterns-of-distributed-systems/P200000011305/9780138221980

A few decades ago, it would have probably seemed strange to put software and automobility together. However, today software is embedded in all kinds of modern vehicles, enabling capabilities in everything from driving to passenger entertainment. But what exactly does this all mean for the automotive industry? And what demands does it place on design and manufacturing processes?

In this episode of the Technology Podcast, two Thoughtworks experts on software-defined vehicles — Michael Fait and Sriram J. — speak to Ashok Subramanian and Ken Mugrage about how the automotive industry has been changed by software. They cover everything from the implications software has for the way we think about design, manufacturing and regulation across the industry to the skills and practices developers need to work in this exciting space.

Is it really possible to measure the impact engineering teams have on a business' success? At a time when growth is challenging for many organizations and questions about productivity and effectiveness dominate industry conversations, getting it right is crucial. And although the DORA metrics are today well-established and extremely useful is it really enough? Do they actually help us tie the work we do to tangible business results?

In attempting to answer these questions, a group of Thoughtworkers have developed what they call EEBO metrics. These are designed to measure engineering excellence to business outcomes.

To discuss EEBO metrics, hosts Prem Chandrasekaran and Scott Shaw (CTO, Thoughtworks APAC) are joined by Dinker Charak (Principal Product Strategist) and Sachin Dharmapurikar (Global Product Manager). Charak and Dharmapurikar helped to develop EEBO metrics; they believe it can be a valuable tool in aligning often complex engineering projects and activities with high-level business goals and objectives. Listen as they explain what EEBO metrics are (and aren't) and how businesses should think about using them.