Astronomers are increasingly turning to machine learning as a means to understand more about our universe — whether that’s the formation of galaxies or the Sun’s activity. Here, our co-hosts Neal Ford and Rebecca Parsons catch up with our special guest from the National Center for Radio Astrophysics in Pune, along with a couple of ThoughtWorkers to hear more about this intersection of data science and astrophysics.

Machine learning in astrophysics

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Despite occasional confusion, the difference between continuous delivery and continuous deployment is simple: should deploying to production be on demand or every good build? Answering which approach is 'best' is difficult; any attempt at dogmatism is likely to just look foolish, given it is, like many other debates in software development, context-dependent. But that doesn't mean we shouldn't try and unpick the issues at the heart of the discussion. It's all well and good saying the debate is context-dependent, but what does that actually mean in practice? In this episode of the Technology Podcast, Ken Mugrage and Valentina Servile debate the merits of both continuous delivery and continuous deployment. Talking with hosts Prem Chandrasekaran and Birgitta Böckeler, they offer their perspectives on when and where both should be used — in making the case for their chosen approaches, they shed some much needed light on a discussion that every software engineering team should have.   Learn more about Valentina Servile's book Continuous Deployment: https://www.thoughtworks.com/insights/books/continuous-deployment

Continuous delivery vs. continuous deployment: What should be the default?

Volume 30 of the Thoughtworks Technology Radar was published in April 2024. Alongside 105 blips, the edition also featured four themes selected by the team of technologists that puts the Radar together. They were: open-ish source licenses, AI-assisted software development teams, emerging architecture patterns for LLMs and dragging pull requests closer to continuous integration. Each one cuts across the technologies and techniques included on the Radar and highlights a key issue or challenge for software developers — and other technologists — working today. In this episode of the Technology Podcast, Birgitta Böckeler and Erik Dörnenberg join Neal Ford and Ken Mugrage to discuss the themes for Technology Radar Vol.30. They explain what they mean, why they were picked and what their implications are for the wider industry.   Explore volume 30 of the Technology Radar: https://www.thoughtworks.com/radar

Themes from Technology Radar Vol.30

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  

Building at the intersection of machine learning and software engineering

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

Refactoring with AI

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/    

Machine learning in astrophysics

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