AI Engineering Podcast

By: Tobias Macey
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  • Summary

  • 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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Episodes
  • Enhancing AI Retrieval with Knowledge Graphs: A Deep Dive into GraphRAG

    Enhancing AI Retrieval with Knowledge Graphs: A Deep Dive into GraphRAG
    Sep 10 2024
    SummaryIn this episode of the AI Engineering podcast, Philip Rathle, CTO of Neo4J, talks about the intersection of knowledge graphs and AI retrieval systems, specifically Retrieval Augmented Generation (RAG). He delves into GraphRAG, a novel approach that combines knowledge graphs with vector-based similarity search to enhance generative AI models. Philip explains how GraphRAG works by integrating a graph database for structured data storage, providing more accurate and explainable AI responses, and addressing limitations of traditional retrieval systems. The conversation covers technical aspects such as data modeling, entity extraction, and ontology use cases, as well as the infrastructure and workflow required to support GraphRAG, setting the stage for innovative applications across various industries.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 Philip Rathle about the application of knowledge graphs in AI retrieval systemsInterviewIntroductionHow did you get involved in machine learning?Can you describe what GraphRAG is?What are the capabilities that graph structures offer beyond vector/similarity-based retrieval methods of prompting?What are some examples of the ways that semantic limitations of nearest-neighbor vector retrieval fail to provide relevant results?What are the technical requirements to implement graph-augmented retrieval?What are the concrete ways in which the embedding and retrieval steps of a typical RAG pipeline need to be modified to account for the addition of the graph?Many tutorials for building vector-based knowledge repositories skip over considerations around data modeling. For building a graph-based knowledge repository there obviously needs to be a bit more work put in. What are the key design choices that need to be made for implementing the graph for an AI application?How does the selection of the ontology/taxonomy impact the performance and capabilities of the resulting application?Building a fully functional knowledge graph can be a significant undertaking on its own. How can LLMs and AI models help with the construction and maintenance of that knowledge repository?What are some of the validation methods that should be brought to bear to ensure that the resulting graph properly represents the knowledge domain that you are trying to model?Vector embedding and retrieval are a core building block for a majority of AI application frameworks. How much support do you see for GraphRAG in the ecosystem?For the case where someone is using a framework that does not explicitly implement GraphRAG techniques, what are some of the implementation strategies that you have seen be most effective for adding that functionality?What are some of the ways that the combination of vector search and knowledge graphs are useful independent of their combination with language models?What are the most interesting, innovative, or unexpected ways that you have seen GraphRAG used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on GraphRAG applications?When is GraphRAG the wrong choice?What are the opportunities for improvement in the design and implementation of graph-based retrieval 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.LinksNeo4JGraphRAG ManifestoRAG == Retrieval Augmented GenerationPodcast EpisodeVLDB == Very Large DataBasesKnowledge GraphNearest Neighbor SearchPageRankThings Not Strings) Google Knowledge Graph PaperpgvectorPineconeData Engineering Podcast EpisodeTables To LabelsNLP == Natural Language ProcessingOntologyLangChainLlamaIndexRLHF == Reinforcement Learning with Human FeedbackSenzingNeoConverseCypher query languageGQL query standardAWS BedrockVertex AISequoia Training Data - Klarna episodeOuroborosThe 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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    59 mins
  • Harnessing Generative AI for Effective Digital Advertising Campaigns

    Harnessing Generative AI for Effective Digital Advertising Campaigns
    Sep 2 2024
    SummaryIn this episode of the AI Engineering podcast Praveen Gujar, Director of Product at LinkedIn, talks about the applications of generative AI in digital advertising. He highlights the key areas of digital advertising, including audience targeting, content creation, and ROI measurement, and delves into how generative AI is revolutionizing these aspects. Praveen shares successful case studies of generative AI in digital advertising, including campaigns by Heinz, the Barbie movie, and Maggi, and discusses the potential pitfalls and risks associated with AI-powered tools. He concludes with insights into the future of generative AI in digital advertising, highlighting the importance of cultural transformation and the synergy between human creativity and AI.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 Praveen Gujar about the applications of generative AI in digital advertisingInterviewIntroductionHow did you get involved in machine learning?Can you start by defining "digital advertising" for the scope of this conversation?What are the key elements/characteristics/goals of digital avertising?In the world before generative AI, what did a typical end-to-end advertising campaign workflow look like?What are the stages of that workflow where generative AI are proving to be most useful?How do the current limitations of generative AI (e.g. hallucinations, non-determinism) impact the ways in which they can be used?What are the technological and organizational systems that need to be implemented to effectively apply generative AI in public-facing applications that are so closely tied to brand/company image?What are the elements of user education/expectation setting that are necessary when working with marketing/advertising personnel to help avoid damage to the brands?What are some examples of applications for generative AI in digital advertising that have gone well?Any that have gone wrong?What are the most interesting, innovative, or unexpected ways that you have seen generative AI used in digital advertising?What are the most interesting, unexpected, or challenging lessons that you have learned while working on digital advertising applications of generative AI?When is generative AI the wrong choice?What are your future predictions for the use of generative AI in dgital advertising?Contact InfoWebsiteLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning 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.LinksGenerative AILLM == Large Language ModelDall-E)RLHF == Reinforcement Learning fHuman FeedbackThe 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 mins
  • Building Scalable ML Systems on Kubernetes

    Building Scalable ML Systems on Kubernetes
    Aug 15 2024
    SummaryIn this episode of the AI Engineering podcast, host Tobias Macy interviews Tammer Saleh, founder of SuperOrbital, about the potentials and pitfalls of using Kubernetes for machine learning workloads. The conversation delves into the specific needs of machine learning workflows, such as model tracking, versioning, and the use of Jupyter Notebooks, and how Kubernetes can support these tasks. Tammer emphasizes the importance of a unified API for different teams and the flexibility Kubernetes provides in handling various workloads. Finally, Tammer offers advice for teams considering Kubernetes for their machine learning workloads and discusses the future of Kubernetes in the ML ecosystem, including areas for improvement and innovation.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 Tammer Saleh about the potentials and pitfalls of using Kubernetes for your ML workloads.InterviewIntroductionHow did you get involved in Kubernetes?For someone who is unfamiliar with Kubernetes, how would you summarize it?For the context of this conversation, can you describe the different phases of ML that we're talking about?Kubernetes was originally designed to handle scaling and distribution of stateless processes. ML is an inherently stateful problem domain. What challenges does that add for K8s environments?What are the elements of an ML workflow that lend themselves well to a Kubernetes environment?How much Kubernetes knowledge does an ML/data engineer need to know to get their work done?What are the sharp edges of Kubernetes in the context of ML projects?What are the most interesting, unexpected, or challenging lessons that you have learned while working with Kubernetes?When is Kubernetes the wrong choice for ML?What are the aspects of Kubernetes (core or the ecosystem) that you are keeping an eye on which will help improve its utility for ML workloads?Contact InfoEmailLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for ML workloads today?LinksSuperOrbitalCloudFoundryHeroku12 Factor ModelKubernetesDocker ComposeCore K8s ClassJupyter NotebookCrossplaneOchre JellyCNCF (Cloud Native Computing Foundation) LandscapeStateful SetRAG == Retrieval Augmented GenerationPodcast EpisodeKubeflowFlyteData Engineering Podcast EpisodePachydermData Engineering Podcast EpisodeCoreWeaveKubectl ("koob-cuddle")HelmCRD == Custom Resource DefinitionHorovodPodcast.__init__ EpisodeTemporalSlurmRayDaskInfinibandThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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    50 mins
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