We Can All Be AI Engineers and We Can Do It with Open Source Models // Luke Marsden // #273

Luke Marsden, is a passionate technology leader. Experienced in consultant, CEO, CTO, tech lead, product, sales, and engineering roles. Proven ability to conceive and execute a product vision from strategy to implementation, while iterating on product-market fit.We Can All Be AI Engineers and We Can Do It with Open Source Models // MLOps Podcast #273 with Luke Marsden, CEO of HelixML.// AbstractIn this podcast episode, Luke Marsden explores practical approaches to building Generative AI applications using open-source models and modern tools. Through real-world examples, Luke breaks down the key components of GenAI development, from model selection to knowledge and API integrations, while highlighting the data privacy advantages of open-source solutions.// BioHacker & entrepreneur. Founder at helix.ml. Career spanning DevOps, MLOps, and now LLMOps. Working on bringing business value to local, open-source LLMs.// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksWebsite: https://helix.mlAbout open source AI: https://blog.helix.ml/p/the-open-source-ai-revolutionRatatat Cream on Chrome: https://open.spotify.com/track/3s25iX3minD5jORW4KpANZ?si=719b715154f64a5f --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Luke on LinkedIn: https://www.linkedin.com/in/luke-marsden-71b3789/Timestamps:[00:00] Michael's preferred coffee[00:21] Takeaways[01:59] Please like, share, leave a review, and subscribe to our MLOps channels![02:10] Gaming to AI Accelerators[11:34] Torch Chat goals[18:53] Pytorch benchmarking and competitiveness[21:28] Optimizing MLOps models[24:52] GPU optimization tips[29:36] Cloud vs On-device AI[38:22] Abstraction across devices [42:29] PyTorch developer experience[45:33] AI and MLOps-related antipatterns[48:33] When to optimize[53:26] Efficient edge AI models[56:57] Wrap up