Links
- Codecrafters (sponsor): https://tej.as/codecrafters
- Julia's Talk: https://youtu.be/IFn2hMt480M?si=x0-2M2IBOASwaicz
- TomTom: https://tomtom.com
- Julia on LinkedIn: https://www.linkedin.com/in/juliawallin/
- Tejas on X: https://x.com/tejaskumar_
Summary
In this podcast episode, we discuss the evolving landscape of AI engineering, data science, and data engineering. Julia and I explore the definitions and distinctions between these roles, delve into the intricacies of clustering and classification, and examine the role of MLOps in deploying machine learning models.
Julia shares insights into her work at TomTom, highlighting the company's transition from hardware to software and the innovative data collection techniques they employ, including LiDAR technology and OpenStreetMap.
Chapters
00:00:00 Introduction
00:11:46 Data Science and Data Engineering
00:21:01 Role at TomTom and Road Furniture Features Detection
00:34:18 Importance of Speed Limits and Fusion Algorithm
00:43:19 Defining HD Maps and Their Importance
00:54:16 Exploring Prototyping and Real-Time Updates
01:03:02 Importance of Smaller Models
01:19:30 Future of Mapping and AI in Transportation
01:29:14 Lessons for Early Career Professionals
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