[{"data":1,"prerenderedAt":69},["ShallowReactive",2],{"/hall-of-fame/maximilian-hudlberger":3},{"speaker":4,"podcastCount":67,"pickOfTheDayCount":68},{"id":5,"sort":6,"slug":7,"academic_title":6,"first_name":8,"last_name":9,"occupation":10,"description":11,"website_url":12,"bluesky_url":6,"twitter_url":13,"linkedin_url":14,"youtube_url":6,"github_url":6,"instagram_url":6,"profile_image":15,"podcasts":27,"picks_of_the_day":48,"tags":64,"tagsPrepared":65,"podcastsPrepared":66},"bcc96926-bb8a-47f9-8581-1dedf44e86e9",null,"maximilian-hudlberger","Maximilian","Hudlberger","Deployment Engineer bei OpenAI","\u003Cp>Max ist Teil des OpenAI Gründungsteams in Deutschland und hilft Unternehmens und Entwicklern, aus KI echte Produkte zu machen. Sein Weg begann nach dem Mathematikstudium im traditionellen Machine Learning Engineering und führte ihn von Predictive AI zu Generative AI, wobei er besonders darauf setzt, Qualität belastbar zu messen und iterativ zu verbessern. \u003C/p>\n\u003Cp>Max ist regelmäßig im deutschen KI-Ökosystem unterwegs und unterstützt Formate wie Student Hackathons sowie Builder- und Community-Formate.\u003C/p>","https://developers.openai.com","https://x.com/MaxHudlberger","https://www.linkedin.com/in/maximilian-hudlberger-32b06b129/",{"id":16,"title":17,"description":6,"tags":6,"location":6,"filename_disk":18,"filename_download":19,"metadata":20,"type":21,"filesize":22,"modified_by":6,"modified_on":23,"embed":6,"uploaded_by":24,"uploaded_on":25,"folder":6,"width":26,"height":26,"charset":6,"duration":6},"2c48da90-5cba-4348-a78f-f7726916259f","Max","2c48da90-5cba-4348-a78f-f7726916259f.png","max.png",{},"image/png","3672933","2026-03-16T12:51:13.823Z","11a655b6-bcfc-4c6c-a13c-68189e38edb9","2026-03-16T12:51:13.819Z",2676,[28],{"podcast":29},{"id":30,"slug":31,"published_on":32,"type":33,"number":34,"title":35,"audio_url":36,"cover_image":37},"15067f69-b1cf-4fbe-8f26-6aa130be655c","deep-dive-203-ai-in-production-mit-maximilian-hudlberger","2026-03-17T05:00:00.000Z","deep_dive","203","AI in Production mit Maximilian Hudlberger","https://www.buzzsprout.com/176239/episodes/18854456-deep-dive-203-ai-in-production-mit-maximilian-hudlberger.mp3",{"id":38,"title":39,"description":6,"tags":6,"location":6,"filename_disk":40,"filename_download":41,"metadata":42,"type":43,"filesize":44,"modified_by":6,"modified_on":45,"embed":6,"uploaded_by":6,"uploaded_on":46,"folder":6,"width":47,"height":47,"charset":6,"duration":6},"4e9176e3-a11e-41b0-8c73-a18688df99ce","AI in Production mit Maximilian Hudlberger - episode_cover","4e9176e3-a11e-41b0-8c73-a18688df99ce.jpg","deep_dive-203-episode_cover-1773665813121.jpg",{},"image/jpeg","487147","2026-03-16T12:56:53.259Z","2026-03-16T12:56:53.257Z",1024,[49],{"id":50,"name":51,"website_url":52,"description":53,"image":54},"48266301-14ca-4921-a1a2-3ea86884097a","AI Engineering von Chip Huyen","https://www.hugendubel.de/de/buch_kartoniert/chip_huyen-ai_engineering-48763844-produkt-details.html","\u003Cp>„AI Engineering“ von Chip Huyen ist ein praxisorientierter Leitfaden dafür, wie man KI‑Applikationen mit Foundation Models systematisch entwickelt und in die Produktion bringt. Das Buch stellt den kompletten Lebenszyklus von KI‑Projekten vor — von der Auswahl von Modellen, Daten und Evaluationsmetriken über Evaluationsmethoden, Prompt‑ und RAG‑Techniken bis hin zu Fine‑Tuning, Agents und der Optimierung von Latenz und Kosten.\u003C/p>\n\u003Cp>Es liefert eine klare Framework‑Struktur für Entwickler:innen, die mit generativen Modellen robuste und skalierbare Lösungen bauen wollen.\u003C/p>",{"id":55,"title":6,"description":6,"tags":6,"location":6,"filename_disk":56,"filename_download":57,"metadata":58,"type":43,"filesize":59,"modified_by":6,"modified_on":60,"embed":6,"uploaded_by":24,"uploaded_on":61,"folder":6,"width":62,"height":63,"charset":6,"duration":6},"890435cb-800a-4f60-ac9a-b17e1fcf20d1","890435cb-800a-4f60-ac9a-b17e1fcf20d1.jpg","www-hugendubel-de-de-buchkartoniert-chiphuyen-aiengineering-48763844-produkt-details-html.jpg",{},"78510","2026-02-06T08:52:03.718Z","2026-02-06T08:52:03.715Z",1366,768,[],[],[29],495,458,1774369159715]