SE Radio 677: Jacob Visovatti and Conner Goodrum on Testing ML Models for Enterprise Products
Jacob Visovatti and Conner Goodrum of Deepgram speak with host Kanchan Shringi about testing ML models for enterprise use and why it's critical for product reliability and quality. They discuss the challenges of testing machine learning models in enterprise environments, especially in foundational AI contexts. The conversation particularly highlights the differences in testing needs between companies that build ML models from scratch and those that rely on existing infrastructure. Jacob and Conner describe how testing is more complex in ML systems due to unstructured inputs, varied data distribution, and real-time use cases, in contrast to traditional software testing frameworks such as the testing pyramid. To address the difficulty of ensuring LLM quality, they advocate for iterative feedback loops, robust observability, and production-like testing environments. Both guests underscore that testing and quality assurance are interdisciplinary efforts that involve data scientists, ML engineers, software engineers, and product managers. Finally, this episode touches on the importance of synthetic data generation, fuzz testing, automated retraining pipelines, and responsible model deployment—especially when handling sensitive or regulated enterprise data. Brought to you by IEEE Computer Society and IEEE Software magazine.
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SE Radio 676: Samuel Colvin on the Pydantic Ecosystem
Samuel Colvin, the CEO and founder of Pydantic, speaks with host Gregory M. Kapfhammer about the ecosystem of Pydantic’s Python frameworks, including Pydantic, Pydantic AI, and Pydantic Logfire. Along with discussing the design, implementation, and use of these frameworks, they dive into the refactoring of Pydantic and the follow-on performance improvements. They also explore ways in which Python programmers can use these three frameworks to build, test, evaluate, and monitor their own applications that interact with both local and cloud-based large language models. Brought to you by IEEE Computer Society and IEEE Software magazine.
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SE Radio 675: Brian Demers on Observability into the Toolchain
Brian Demers, Developer Advocate at Gradle, speaks with host Giovanni Asproni about the importance of having observability in the toolchain. Such information about build times, compiler warnings, test executions, and any other system used to build the production code can help to reduce defects, increase productivity, and improve the developer experience. During the conversation they touch upon what is possible with today’s tools; the impact on productivity and developer experience; and the impact, both in terms of risks and opportunities, introduced by the use of artificial intelligence. Brought to you by IEEE Computer Society and IEEE Software magazine.
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SE Radio 674: Vilhelm von Ehrenheim on Autonomous Testing
Vilhelm von Ehrenheim, co-founder and chief AI officer of QA.tech, speaks with SE Radio's Brijesh Ammanath about autonomous testing. The discussion starts by covering the fundamentals, and how testing has evolved from manual to automated to now autonomous. Vilhelm then deep dives into the details of autonomous testing and the role of agents in autonomous testing. They consider the challenges in adopting autonomous testing, and Wilhelm describes the experiences of some clients who have made the transition. Toward the end of the show, Vilhelm describes the impact of autonomous testing on the traditional QA career and what test professionals can do to upskill. This episode is sponsored by Fly.io.
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SE Radio 673: Abhinav Kimothi on Retrieval-Augmented Generation
In this episode of Software Engineering Radio, Abhinav Kimothi sits down with host Priyanka Raghavan to explore retrieval-augmented generation (RAG), drawing insights from Abhinav's book, A Simple Guide to Retrieval-Augmented Generation. The conversation begins with an introduction to key concepts, including large language models (LLMs), context windows, RAG, hallucinations, and real-world use cases. They then delve into the essential components and design considerations for building a RAG-enabled system, covering topics such as retrievers, prompt augmentation, indexing pipelines, retrieval strategies, and the generation process. The discussion also touches on critical aspects like data chunking and the distinctions between open-source and pre-trained models. The episode concludes with a forward-looking perspective on the future of RAG and its evolving role in the industry. Brought to you by IEEE Computer Society and IEEE Software magazine.
About Software Engineering Radio - the podcast for professional software developers
Software Engineering Radio is a podcast targeted at the professional software developer. The goal is to be a lasting educational resource, not a newscast. SE Radio covers all topics software engineering. Episodes are either tutorials on a specific topic, or an interview with a well-known character from the software engineering world. All SE Radio episodes are original content — we do not record conferences or talks given in other venues. Each episode comprises two speakers to ensure a lively listening experience. SE Radio is brought to you by the IEEE Computer Society and IEEE Software magazine.
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