The Role of Software Engineering and Ops in AI

This chapter discusses the importance of software engineering and ops in the field of AI, debunking the misconception that AI engineers can solely rely on APIs. It highlights the need for expertise in tasks such as data engineering and platform management. The chapter also touches on the trade-offs between using external APIs and maintaining data privacy.

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Paco Nathan is the Managing Partner at Derwen, Inc., and author of Latent Space, along with other books, plus popular videos and tutorials about machine learning, natural language, graph technologies, and related topics.

MLOps podcast #201 with Paco Nathan, Managing Partner at Derwen, Inc., Language, Graphs, and AI in Industry.

// Abstract

Let's talk about key findings from these conferences, specifically summarizing teams that have ROI on machine learning in production: what are the things in common they're doing, and what are the most important caveats they urge other teams to consider when getting started? Because these key takeaways aren't found in the current AI news cycle.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

AI Conference: https://aiconference.com/

K1st World: https://www.k1st.world/

Corunna Innovation Summit: https://corunna.dataspartan.com/

"Cloud Computing on Amazon AWS EC2" UC Berkeley EECS guest lecture (2009)

https://vimeo.com/manage/videos/3616394

"Hardware - Software - Process: Data Science in a Post-Moore’s Law World"

https://www.nvidia.com/en-us/ai-data-science/resources/hardware-software-process-book/