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AI Engineering Podcast

AI Engineering Podcast

Auteur(s): Tobias Macey
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 This show is your guidebook to building scalable and maintainable AI systems. You will learn how to architect AI applications, apply AI to your work, and the considerations involved in building or customizing new models. Everything that you need to know to deliver real impact and value with machine learning and artificial intelligence.© 2024 Boundless Notions, LLC.
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  • From GPUs to Workloads: Flex AI’s Blueprint for Fast, Cost‑Efficient AI

    From GPUs to Workloads: Flex AI’s Blueprint for Fast, Cost‑Efficient AI
    Sep 28 2025
    SummaryIn this episode of the AI Engineering Podcast Brijesh Tripathi, CEO of Flex AI, talks about revolutionizing AI engineering by removing DevOps burdens through "workload as a service". Brijesh shares his expertise from leading AI/HPC architecture at Intel and deploying supercomputers like Aurora, highlighting how access friction and idle infrastructure slow progress. He discusses Flex AI's innovative approach to simplifying heterogeneous compute, standardizing on consistent Kubernetes layers, and abstracting inference across various accelerators, allowing teams to iterate faster without wrestling with drivers, libraries, or cloud-by-cloud differences. Brijesh also shares insights into Flex AI's strategies for lifting utilization, protecting real-time workloads, and spanning the full lifecycle from fine-tuning to autoscaled inference, all while keeping complexity at bay.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsWhen ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.Your host is Tobias Macey and today I'm interviewing Brijesh Tripathi about FlexAI, a platform offering a service-oriented abstraction for AI workloadsInterviewIntroductionHow did you get involved in machine learning?Can you describe what FlexAI is and the story behind it?What are some examples of the ways that infrastructure challenges contribute to friction in developing and operating AI applications?How do those challenges contribute to issues when scaling new applications/businesses that are founded on AI?There are numerous managed services and deployable operational elements for operationalizing AI systems. What are some of the main pitfalls that teams need to be aware of when determining how much of that infrastructure to own themselves?Orchestration is a key element of managing the data and model lifecycles of these applications. How does your approach of "workload as a service" help to mitigate some of the complexities in the overall maintenance of that workload?Can you describe the design and architecture of the FlexAI platform?How has the implementation evolved from when you first started working on it?For someone who is going to build on top of FlexAI, what are the primary interfaces and concepts that they need to be aware of?Can you describe the workflow of going from problem to deployment for an AI workload using FlexAI?One of the perennial challenges of making a well-integrated platform is that there are inevitably pre-existing workloads that don't map cleanly onto the assumptions of the vendor. What are the affordances and escape hatches that you have built in to allow partial/incremental adoption of your service?What are the elements of AI workloads and applications that you are explicitly not trying to solve for?What are the most interesting, innovative, or unexpected ways that you have seen FlexAI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on FlexAI?When is FlexAI the wrong choice?What do you have planned for the future of FlexAI?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?LinksFlex AIAurora Super ComputerCoreWeaveKubernetesCUDAROCmTensor Processing Unit (TPU)PyTorchTritonTrainiumASIC == Application Specific Integrated CircuitSOC == System On a ChipLoveableFlexAI BlueprintsTenstorrentThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
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    55 min
  • Right-Sizing AI: Small Language Models for Real-World Production

    Right-Sizing AI: Small Language Models for Real-World Production
    Sep 20 2025
    SummaryIn this episode of the AI Engineering Podcast Steven Huels, VP of AI Engineering at Red Hat, talks about the practical applications of small language models (SLMs) for production workloads. He discusses how SLMs offer a pragmatic choice due to their ability to fit on single enterprise GPUs and provide model selection trade-offs. The conversation covers self-hosting vs using API providers, organizational capabilities needed for running production-grade LLMs, and the importance of guardrails and automated evaluation at scale. They also explore the rise of agentic systems and service-oriented approaches powered by smaller models, highlighting advances in customization and deployment strategies. Steven shares real-world examples and looks to the future of agent cataloging, continuous retraining, and resource efficiency in AI engineering.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsWhen ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.Your host is Tobias Macey and today I'm interviewing Steven Huels about the benefits of small language models for production workloadsInterviewIntroductionHow did you get involved in machine learning?Language models are available in a wide range of sizes, measured both in terms of parameters and disk space. What are your heuristics for deciding what qualifies as a "small" vs. "large" language model?What are the corresponding heuristics for when to use a small vs. large model?The predominant use case for small models is in self-hosted contexts, which requires a certain amount of organizational sophistication. What are some helpful questions to ask yourself when determining whether to implement a model-serving stack vs. relying on hosted options?What are some examples of "small" models that you have seen used effectively?The buzzword right now is "agentic" for AI driven workloads. How do small models fit in the context of agent-based workloads?When and where should you rely on larger models?When speaking of small models, one of the common requirements for making them truly useful is to fine-tune them for your problem domain and organizational data. How has the complexity and difficulty of that operation changed over the past ~2 years?Serving models requires several operational capabilities beyond the raw inference serving. What are the other infrastructure and organizational investments that teams should be aware of as they embark on that path?What are the most interesting, innovative, or unexpected ways that you have seen small language models used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on operationalizing inference and model customization?When is a small or self-hosted language model the wrong choice?What are your predictions for the near future of small language model capabilities/availability?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksRedHat AI EngineeringGenerative AIPredictive AIChatGPTQLORAHuggingFacevLLMOpenShift AILlama ModelsDeepSeekGPT-OSSMistralMixture of Experts (MoE)QwenInstructLabSFT == Supervised Fine TuningLORAThe intro and outro music is...
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    51 min
  • AI Agents and Identity Management

    AI Agents and Identity Management
    Sep 13 2025
    SummaryIn this episode of the AI Engineering Podcast Julianna Lamb, co-founder and CTO of Stytch, talks about the complexities of managing identity and authentication in agentic workflows. She explores the evolving landscape of identity management in the context of machine learning and AI, highlighting the importance of flexible compute environments and seamless data exchange. The conversation covers implications of AI agents on identity management, including granular permissions, OAuth protocol, and adapting systems for agentic interactions. Julianna also discusses rate limiting, persistent identity, and evolving standards for managing identity in AI systems. She emphasizes the need to experiment with AI agents and prepare systems for integration to stay ahead in the rapidly advancing AI landscape.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsWhen ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.Your host is Tobias Macey and today I'm interviewing Julianna Lamb about the complexities of managing identity and auth in agentic workflowsInterviewIntroductionHow did you get involved in machine learning?The term "identity" is very overloaded. Can you start by giving your definition in the context of technical systems?What are some of the different ways that AI agents intersect with identity?We have decades of experience and effort in building identity infrastructure for the internet, what are the most significant ways in which that is insufficient for agent-based use cases?I have heard anecdotal references to the ways in which AI agents lead to a proliferation of "identities". How would you characterize the magnitude of the difference in scale between human-powered identity, deterministic automation (e.g. bots or bot-nets), and AI agents?The other major element of establishing and verifying "identity" is how that intersects with permissions or authorization. What are the major shortcomings of our existing investment in managing and auditing access and control once you are within a system?How does that get amplified with AI agents?Typically authentication has been done at the perimeter of a system. How does that architecture change when accounting for AI agents?How does that get complicated by where the agent originates? (e.g external agents interacting with a third-party system vs. internal agents operated by the service provider)What are the concrete steps that engineering teams should be taking today to start preparing their systems for agentic use-cases (internal or external)?How do agentic capabilities change the means of protecting against malicious bots? (e.g. bot detection, defensive agents, etc.)What are the most interesting, innovative, or unexpected ways that you have seen authn/authz/identity addressed for AI use cases?What are the most interesting, unexpected, or challenging lessons that you have learned while working on identity/auth(n|z) systems?What are your predictions for the future of identity as adoption and sophistication of AI systems progresses?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksStytchAI AgentMachine To Machine ...
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    54 min
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