AI Engineering Podcast

SummaryIn this episode Guillaume de Saint Marc, VP of Engineering at Cisco Outshift, talks about the complexities and opportunities of scaling multi‑agent systems. Guillaume explains why specialized agents collaborating as a team inspire trust in enterprise settings, and contrasts rigid, “lift-and-shift” agentic workflows with fully self-forming systems. We explore the emerging Internet of Agents, the need for open, interoperable protocols (A2A for peer collaboration and MCP for tool calling), and new layers in the stack for syntactic and semantic communication. Guillaume details foundational needs around discovery, identity, observability, and fine-grained, task/tool/transaction-based access control (TBAC), along with Cisco’s open-source Agency initiative, directory concepts, and OpenTelemetry extensions for agent traces. He shares concrete wins in IT/NetOps—network config validation, root-cause analysis, and the CAPE platform engineer agent—showing dramatic productivity gains. We close with human-in-the-loop UX patterns for multi-agent teams and SLIM, a high-performance group communication layer designed for agent collaboration.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.Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Guillaume de Saint Marc about the complexities and opportunities of scaling multi-agent systemsInterviewIntroductionHow did you get involved in machine learning?Can you start by giving an overview of what constitutes a "multi-agent" system?Many of the multi-agent services that I have read or spoken about are designed and operated by a single department or organization. What are some of the new challenges that arise when allowing agents to communicate and co-ordinate outside of organizational boundaries?The web is the most famous example of a successful decentralized system, with HTTP being the most ubiquitous protocol powering it. What does the internet of agents look like?What is the role of humans in that equation?The web has evolved in a combination of organic and planned growth and is vastly more complex and complicated than when it was first introduced. What are some of the most important lessons that we should carry forward into the connectivity of AI agents?Security is a critical aspect of the modern web. What are the controls, assertions, and constraints that we need to implement to enable agents to operate with a degree of trust while also being appropriately constrained?The AGNTCY project is a substantial investment in an open architecture for the internet of agents. What does it provide in terms of building blocks for teams and businesses who are investing in agentic services?What are the most interesting, innovative, or unexpected ways that you have seen AGNTCY/multi-agent systems used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on multi-agent systems?When is a multi-agent system the wrong choice?What do you have planned for the future of AGNTCY/multi-agent systems?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.LinksOutshift by CiscoMulti-Agent SystemsDeep LearningMerakiSymbolic ReasoningTransformer ArchitectureDeepSeekLLM ReasoningRené DescartesKanbanA2A (Agent-to-Agent) ProtocolMCP == Model Context ProtocolAGNTCYICANN == Internet Corporation for Assigned Names and NumbersOSI LayersOCI == Open Container InitiativeOASF == Open Agentic Schema FrameworkOracle AgentSpecSplunkOpenTelemetryCAIPE == Community AI Platform EngineerAGNTCY Coffee ShopThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryIn this episode of the AI Engineering Podcast Daniel Sodickson, Chief of Innovation in Radiology at NYU Grossman School of Medicine, talks about harnessing AI systems to truly understand images and revolutionize science and healthcare. Dan shares his journey from linear reconstruction to early deep learning for accelerated MRI, highlighting the importance of domain expertise when adapting models to specialized modalities. He explores "upstream" AI that changes what and how we measure, using physics-guided networks, prior knowledge, and personal baselines to enable faster, cheaper, and more accessible imaging. The conversation covers multimodal world models, cross-disciplinary translation, explainability, and a future where agents flag abnormalities while humans apply judgment, as well as provocative frontiers like "imaging without images," continuous health monitoring, and decoding brain activity. Dan stresses the need to preserve truth, context, and human oversight in AI-driven imaging, and calls for tools that distill core methodologies across disciplines to accelerate understanding and progress.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.Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Daniel Sodickson about the impact and applications of AI that is capable of image understandingInterviewIntroductionHow did you get involved in machine learning?Images and vision are concepts that we understand intuitively, but which have a large potential semantic range. How would you characterize the scope and application of imagery in the context of AI and other autonomous technologies?Can you give an overview of the current state of image/vision capabilities in AI systems?A predominant application of machine vision has been for object recognition/tracking. How are advances in AI changing the range of problems that can be solved with computer vision systems?A substantial amount of work has been done on processing of images such as the digital pictures taken by smartphones. As you move to other types of image data, particularly in non-visible light ranges, what are the areas of similarity and in what ways do we need to develop new processing/analysis techniques?What are some of the ways that AI systems will change the ways that we conceive of What are the most interesting, innovative, or unexpected ways that you have seen AI vision used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on imaging technologies and techniques?When is AI the wrong choice for vision/imaging applications?What are your predictions for the future of AI image understanding?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.LinksMRI == Magnetic Resonance ImagingLinear AlgorithmNon-Linear AlgorithmCompressed SensingDictionary Learning AlgorithmDeep LearningCT ScanCambrian ExplosionLIDAR Point CloudSynthetic Aperture RadarGeoffrey HintonCo-Intelligence by Ethan Mollick (affiliate link)TomographyX-Ray CrystallographyCERNCLIP ModelPhysics-Guided Neural NetworkFunctional MRIA Path Toward Autonomous Machine Intelligence by Yann LeCunThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryIn this episode of the AI Engineering Podcast Lucas Thelosen and Drew Gillson talk about Orion, their agentic analytics platform that delivers proactive, push-based insights to business users through asynchronous thinking with rich organizational context. Lucas and Drew share their approach to building trustworthy analysis by grounding in semantic layers, fact tables, and quality-assurance loops, as well as their focus on accuracy through parallel test-time compute and evolving from probabilistic steps to deterministic tools. They discuss the importance of context engineering, multi-agent orchestration, and security boundaries for enterprise deployments, and share lessons learned on consistency, tool design, user change management, and the emerging role of "AI manager" as a career path. The conversation highlights the future of AI knowledge workers collaborating across organizations and tools while simplifying UIs and raising the bar on actionable, trustworthy analytics.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 Lucas Thelosen and Drew Gillson about their experiences building an agentic analytics platform and the challenges of ensuring accuracy to build trustInterviewIntroductionHow did you get involved in machine learning?Can you describe what Orion is and the story behind it?Business analytics is a field that requires a high degree of accuracy and detail because of the potential for substantial impact on the business (positive and negative). These are areas that generative AI has struggled with achieving consistently. What was your process for building confidence in your ability to achieve that threshold before committing to the path you are on now?There are numerous ways that generative AI can be incorporated into the process of designing, building, and delivering analytical insights. How would you characterize the different strategies that data teams and vendors have approached that problem?What do you see as the organizational benefits of moving to a push-based model for analytics?Can you describe the system architecture of Orion?Agentic design patterns are still in the early days of being developed and proven out. Can you give a breakdown of the approach that you are using?How do you think about the responsibility boundaries, communication paths, temporal patterns, etc. across the different agents?Tool use is a key component of agentic architectures. What is your process for identifying, developing, validating, and securing the tools that you provide to your agents?What are the boundaries and extension points that you see when building agentic systems? What are the opportunities for using e.g. A2A for protocol for managing agentic hand-offs?What is your process for managing the experimentation loop for changes to your models, data, prompts, etc. as you iterate on your product?What are some of the ways that you are using the agents that power your system to identify and act on opportunities for self-improvement?What are the most interesting, innovative, or unexpected ways that you have seen Orion used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Orion?When is an agentic approach the wrong choice?What do you have planned for the future of Orion?Contact InfoLucasLinkedInDrewLinkedInParting 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.LinksGravityOrion Data Engineering Podcast EpisodeSite Reliability EngineeringAnthropic Claude Sonnet 4.5A2A (Agent2Agent) ProtocolSimon WillisonAI Lethal TrifectaBehavioral ScienceGrounded TheoryLLM as a JudgeRLHF == Reinforcement Learning from Human FeedbackThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

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

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 AuthenticationAPI AuthenticationMCP == Model Context ProtocolOAuthIdentity ProviderOAuth ScopesOAuth 2.1CaptchaRBAC == Role-Based Access ControlABAC == Attribute-Based Access ControlReBAC == Relationship-Based Access ControlGoogle ZanzibarIdempotenceDynamic Client RegistrationLarge Action ModelsClaude CodeThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryIn this episode of the AI Engineering Podcast Jeremiah Lowin, founder and CEO of Prefect Technologies, talks about the FastMCP framework and the design of MCP servers. Jeremiah explains the evolution of FastMCP, from its initial creation as a simpler alternative to the MCP SDK to its current role in facilitating the deployment of AI tools. The discussion covers the complexities of designing MCP servers, the importance of context engineering, and the potential pitfalls of overwhelming AI agents with too many tools. Jeremiah also highlights the importance of simplicity and incremental adoption in software design, and shares insights into the future of MCP and the broader AI ecosystem. The episode concludes with a look at the challenges of authentication and authorization in AI applications and the exciting potential of MCP as a protocol for the future of AI-driven business logic.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsYour host is Tobias Macey and today I'm interviewing Jeremiah Lowin about the FastMCP framework and how to design and build your own MCP serversInterviewIntroductionHow did you get involved in machine learning?Can you start by describing what MCP is and its purpose in the ecosystem of AI applications?What is FastMCP and what motivated you to create it?Recognizing that MCP is relatively young, how would you characterize the landscape of MCP frameworks?What are some of the stumbling blocks on the path to building a well engineered MCP server?What are the potential ramifications of poorly designed and implemented MCP implementations?In the overall context of an AI-powered/agentic application, what are the tradeoffs of investing in the MCP protocol? (e.g. engineering effort, process isolation, tool creation, auth(n|z), etc.)In your experience, what are the architectural patterns that you see of MCP implementation and usage?There are a multitude of MCP servers available for a variety of use cases. What are the key factors that someone should be using to evaluate their viability for a production use case?Can you give an overview of the key characteristics of FastMCP and why someone might select it as their implementation target for a custom MCP server?How have the design, scope, and goals of the project evolved since you first started working on it?For someone who is using FastMCP as the framework for creating their own AI tools, what are some of the design considerations or best practices that they should be aware of?What are some of the ways that someone might consider integrating FastMCP into their existing Python-powered web applications (e.g. FastAPI, Django, Flask, etc.)As you continue to invest your time and energy into FastMCP, what is your overall goal for the project?What are the most interesting, innovative, or unexpected ways that you have seen FastMCP used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on FastMCP?When is FastMCP the wrong choice?What do you have planned for the future of FastMCP?Contact InfoLinkedInGitHubParting 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.LinksFastMCPFastMCP CloudPrefectModel Context Protocol (MCP)AI ToolsFastAPIPython DecoratorWebsocketsSSE == Server-Sent EventsStreamable HTTPOAuthMCP GatewayMCP SamplingFlaskDjangoASGIMCP ElicitationAuthKitDynamic Client RegistrationsmolagentsLarge Active ModelsA2AThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryIn this episode of the AI Engineering Podcast machine learning engineer Shashank Kapadia explores the transformative role of generative AI in retail. Shashank shares his journey from an engineering background to becoming a key player in ML, highlighting the excitement of understanding human behavior at scale through AI. He discusses the challenges and opportunities presented by generative AI in retail, where it complements traditional ML by enhancing explainability and personalization, predicting consumer needs, and driving autonomous shopping agents and emotional commerce. Shashank elaborates on the architectural and operational shifts required to integrate generative AI into existing systems, emphasizing orchestration, safety nets, and continuous learning loops, while also addressing the balance between building and buying AI solutions, considering factors like data privacy and customization.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsYour host is Tobias Macey and today I'm interviewing Shashank Kapadia about applications of generative AI in retailInterviewIntroductionHow did you get involved in machine learning?Can you summarize the main applications of generative AI that you are seeing the most benefit from in retail/ecommerce?What are the major architectural patterns that you are deploying for generative AI workloads?Working at an organization like WalMart, you already had a substantial investment in ML/MLOps. What are the elements of that organizational capability that remain the same, and what are the catalyzed changes as a result of generative models?When working at the scale of Walmart, what are the different types of bottlenecks that you encounter which can be ignored at smaller orders of magnitude?Generative AI introduces new risks around brand reputation, accuracy, trustworthiness, etc. What are the architectural components that you find most effective in managing and monitoring the interactions that you provide to your customers?Can you describe the architecture of the technical systems that you have built to enable the organization to take advantage of generative models?What are the human elements that you rely on to ensure the safety of your AI products?What are the most interesting, innovative, or unexpected ways that you have seen generative AI break at scale?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI?When is generative AI the wrong choice?What are your paying special attention to over the next 6 - 36 months in AI?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.LinksWalmart LabsThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryIn this episode of the AI Engineering podcast Anush Elangovan, VP of AI software at AMD, discusses the strategic integration of software and hardware at AMD. He emphasizes the open-source nature of their software, fostering innovation and collaboration in the AI ecosystem, and highlights AMD's performance and capability advantages over competitors like NVIDIA. Anush addresses challenges and opportunities in AI development, including quantization, model efficiency, and future deployment across various platforms, while also stressing the importance of open standards and flexible solutions that support efficient CPU-GPU communication and diverse AI workloads.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsYour host is Tobias Macey and today I'm interviewing Anush Elangovan about AMD's work to expand the playing field for AI training and inferenceInterviewIntroductionHow did you get involved in machine learning?Can you describe what your work at AMD is focused on?A lot of the current attention on hardware for AI training and inference is focused on the raw GPU hardware. What is the role of the software stack in enabling and differentiating that underlying compute?CUDA has gained a significant amount of attention and adoption in the numeric computation space (AI, ML, scientific computing, etc.). What are the elements of platform risk associated with relying on CUDA as a developer or organization?The ROCm stack is the key element in AMD's AI and HPC strategy. What are the elements that comprise that ecosystem?What are the incentives for anyone outside of AMD to contribute to the ROCm project?How would you characterize the current competitive landscape for AMD across the AI/ML lifecycle stages? (pre-training, post-training, inference, fine-tuning)For teams who are focused on inference compute for model serving, what do they need to know/care about in regards to AMD hardware and the ROCm stack?What are the most interesting, innovative, or unexpected ways that you have seen AMD/ROCm used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AMD's AI software ecosystem?When is AMD/ROCm the wrong choice?What do you have planned for the future of ROCm?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.LinksImageNetAMDROCmCUDAHuggingFaceLlama 3Llama 4QwenDeepSeek R1MI300XNokia SymbianUALink StandardQuantizationHIPIFYROCm TritonAMD Strix HaloAMD EpycLiquid NetworksMAMBA ArchitectureTransformer ArchitectureNPU == Neural Processing Unitllama.cppOllamaPerplexity ScoreNUMA == Non-Uniform Memory AccessvLLMSGLangThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0