AI Engineering Podcast Podcast Por Tobias Macey arte de portada

AI Engineering Podcast

AI Engineering Podcast

De: 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.
Episodios
  • Navigating the AI Landscape: Challenges and Innovations in Retail

    Navigating the AI Landscape: Challenges and Innovations in Retail
    Aug 7 2025
    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
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    52 m
  • The Anti-CRM CRM: How Spiro Uses AI to Transform Sales

    The Anti-CRM CRM: How Spiro Uses AI to Transform Sales
    Jul 21 2025
    SummaryIn this episode of the AI Engineering podcast Adam Honig, founder of Spiro AI, about using AI to automate CRM systems, particularly in the manufacturing sector. Adam shares his journey from running a consulting company focused on Salesforce to founding Spiro, and discusses the challenges of traditional CRM systems where data entry is often neglected. He explains how Spiro addresses this issue by automating data collection from emails, phone calls, and other communications, providing a rich dataset for machine learning models to generate valuable insights. Adam highlights how Spiro's AI-driven CRM system is tailored to the manufacturing industry's unique needs, where sales are relationship-driven rather than funnel-based, and emphasizes the importance of understanding customer interactions and order histories to predict future business opportunities. The conversation also touches on the evolution of AI models, leveraging powerful third-party APIs, managing context windows, and platform dependencies, with Adam sharing insights into Spiro's future plans, including product recommendations and dynamic data modeling approaches.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 Adam Honig about using AI to automate CRM maintenanceInterviewIntroductionHow did you get involved in machine learning?Can you describe what Spiro is and the story behind it?What are the specific challenges posed by the manufacturing industry with regards to sales and customer interactions?How does the type of manufacturing and target customer influence the level of effort and communication involved in the sales and customer service cycles?Before we discuss the opportunities for automation, can you describe the typical interaction patterns and workflows involved in the care and feeding of CRM systems?Spiro has been around since 2014, long pre-dating the current era of generative models. What were your initial targets for improving efficiency and reducing toil for your customers with the aid of AI/ML?How have the generational changes of deep learning and now generative AI changed the ways that you think about what is possible in your product?Generative models reduce the level of effort to get a proof of concept for language-oriented workflows. How are you pairing them with more narrow AI that you have built?Can you describe the overall architecture of your platform and how it has evolved in recent years?While generative models are powerful, they can also become expensive, and the costs are hard to predict. How are you thinking about vendor selection and platform risk in the application of those models?What are the opportunities that you see for the adoption of more autonomous applications of language models in your product? (e.g. agents)What are the confidence building steps that you are focusing on as you investigate those opportunities?What are the most interesting, innovative, or unexpected ways that you have seen Spiro used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI in the CRM space?When is AI the wrong choice for CRM workflows?What do you have planned for the future of Spiro?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.LinksSpiroDeepgramCognee EpisodeAgentic MemoryGraphRAGPodcast EpisodeOpenAI Assistant APIThe 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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    47 m
  • Unlocking AI Potential with AMD's ROCm Stack

    Unlocking AI Potential with AMD's ROCm Stack
    Jun 23 2025
    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
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    42 m
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