Machine Learning Archives - Software Engineering Daily
45 episodes listed below
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Technical interviews about software topics.
Based on 6 episodes we listened to · September 2026
Technical interviews connect AI products to the engineering decisions, infrastructure constraints, and developer workflows beneath them.
Machine Learning Archives - Software Engineering Daily treats AI as engineering work with products, constraints, and architectural choices. Interviews led by Sean Falconer, Leigh Atchison, and Jordy Mon Companies move from a platform's purpose toward the decisions underneath it,…
From JetBrains documentation to SageMaker checkpointing, your AI curiosity gets past the product announcement.
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Short reviews from the PlayNext desk, based on the episodes we processed.
Rob Futrick connects Anaconda’s Python and package-platform roots to the practical demands of AI development.
Anaconda CTO Rob Futrick frames the company’s next stage around AI development, using the idea of an "OS for AI" as the thread through a broad platform conversation.
A grounded episode on how AI may reshape search, browsing, and the e-commerce journey.
Algolia's search-as-a-service story gives this episode a clear way into how AI is changing product discovery online. CTO Sean Mullaney draws on nearly 20 years in e-commerce while explaining the company's work on neural search, vector compression, and search optimization, with…
A grounded discussion of fitting AI into everyday developer workflows.
JetBrains' move to build an AI assistant into its IDEs gives this episode a clear, practical tension: how a familiar developer tool can make room for language models without turning the workflow into a showcase.
A clear, practical tour of why vector databases matter for LLM context and beyond.
Pinecone Vector Database with Marek Golovic gives a measured, useful account of why vector databases have become part of the AI infrastructure conversation.
A practical look at how Vercel is wiring AI into the front end.
Lee Robinson makes a clear case for AI as a practical force in front-end tooling, especially for teams building on Vercel. The conversation connects that idea to the AI SDK, natural-language interfaces, and vZero, which turns prompts into React code using open-source building…
AWS frames SageMaker HyperPod as a practical fix for large-scale model training.
AWS re:Invent 2023 frames this talk, and it stays on SageMaker as an end-to-end machine learning service. HyperPod gets most of the attention, with a practical pitch: easier cluster setup, more efficient training, checkpointing, and built-in visibility through SageMaker Profiler…
Long-form interviews on AI coding agents, infrastructure, and security, plus a recurring SED News roundup.
Founders and engineers on AI coding agents like Claude Code, and supply chain security.
Stack Overflow's Ryan Donovan interviews founders and engineers on AI agents, protocols, and dev tools.
Michael Kennedy interviews Python maintainers on packaging, security, web tools, and AI.
Michael Kennedy and Brian Okken run down the week's Python tooling, typing, and packaging news.
Matt Stratton interviews DevOps practitioners on security, platform engineering, and trusting AI-written code.
Lee Robinson, VP of Product at Vercel, discusses the company's growth with Next.js, its front-end cloud strategy, and AI innovations like the AI SDK and vZero tool.
Recorded on-site at AWS re:Invent 2023 in Las Vegas, host Jordy Mon Companys interviews Ankur Mehrotra, GM of Amazon SageMaker, covering the history and evolution of SageMaker from traditional ML workflows to the Generative AI era.
Rob Fertrick, CTO at Anaconda, joins Leigh Atchison on Software Engineering Daily to explain how Anaconda grew from Continuum Analytics and the Anaconda distribution into a platform with 45 million users.
Sean Mullaney says Algolia can turn a conversation into a vector, retrieve a thousand products, and keep the LLM from inventing prices.
Jody Burchell says JetBrains AI Assistant uses project-wide context, file structure, and libraries to target code help.
Sean Falconer and Marek Golovic use embeddings, HNSW, and retrieval-augmented generation to explain why Pinecone exists as a vector database.
Birgitta Böckeler, ThoughtWorks' global lead for AI-assisted software delivery, explains how large language models transform natural-language requirements into code.
Arun Kumar presents machine learning as a three-stage lifecycle: sourcing, building, and deploying data-driven prediction systems.