Navigating Safety and Predictability in Machine Learning Systems

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This chapter explores the challenges of ensuring safety and predictability in machine learning systems, emphasizing the non-deterministic nature of machine learning outputs and the need for thorough evaluation and expertise to mitigate risks. It discusses the complexities of using data for machine learning models and the critical importance of data formatting and understanding distribution for effective outcomes.

Autonomous vehicle engineering is a huge challenge and requires the integration of many different technologies. A self-driving car needs data from multiple sensors, ML models to process that data, engineering to couple software and mechanical systems, and much more.

Ian Williams is a Senior Staff Software Engineer at Cruise, and before that worked at Google, Lyft, and eBay. He joins the show to talk about the diverse engineering challenges and strategies associated with building self-driving cars.

This episode is hosted by Tyson Kunovsky. Tyson is the co-founder and CEO of AutoCloud, an infrastructure as code platform. He is originally from South Africa, and has a background in software engineering and cloud development. When he’s not busy designing new GitOps workflows, he enjoys skiing, riding motorcycles, and reading sci-fi books. Check the show notes for more information on Tyson’s work, and where to find him.

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