Julia Flament-Wallin: How to Build Maps of the World with AI

ConTejas Code

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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