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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.
// Abstract
In 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.
// Bio
Hacker & entrepreneur. Founder at helix.ml. Career spanning DevOps, MLOps, and now LLMOps. Working on bringing business value to local, open-source LLMs.
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related LinksWebsite: https://helix.ml
About open source AI: https://blog.helix.ml/p/the-open-source-ai-revolution
Ratatat Cream on Chrome: https://open.spotify.com/track/3s25iX3minD5jORW4KpANZ?si=719b715154f64a5f
--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch 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
By Demetrios4.6
2323 ratings
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.
// Abstract
In 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.
// Bio
Hacker & entrepreneur. Founder at helix.ml. Career spanning DevOps, MLOps, and now LLMOps. Working on bringing business value to local, open-source LLMs.
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related LinksWebsite: https://helix.ml
About open source AI: https://blog.helix.ml/p/the-open-source-ai-revolution
Ratatat Cream on Chrome: https://open.spotify.com/track/3s25iX3minD5jORW4KpANZ?si=719b715154f64a5f
--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch 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

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