{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/655148df2861630012a1d01b/675f07f2a89833ab777185e7?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Julia Flament-Wallin: How to Build Maps of the World with AI","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/655148df2861630012a1d01b/1734280358910-169b4d3d-ab40-44c4-9132-71c1d7417642.jpeg?height=200","description":"<p>Links</p><p>- Codecrafters (sponsor): https://tej.as/codecrafters</p><p><br></p><p>- Julia's Talk: https://youtu.be/IFn2hMt480M?si=x0-2M2IBOASwaicz</p><p>- TomTom: https://tomtom.com</p><p>- Julia on LinkedIn: https://www.linkedin.com/in/juliawallin/</p><p>- Tejas on X: https://x.com/tejaskumar_</p><p><br></p><p>Summary</p><p><br></p><p>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. </p><p><br></p><p>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.</p><p><br></p><p>Chapters</p><p><br></p><p>00:00:00 Introduction</p><p>00:11:46 Data Science and Data Engineering</p><p>00:21:01 Role at TomTom and Road Furniture Features Detection</p><p>00:34:18 Importance of Speed Limits and Fusion Algorithm</p><p>00:43:19 Defining HD Maps and Their Importance</p><p>00:54:16 Exploring Prototyping and Real-Time Updates</p><p>01:03:02 Importance of Smaller Models</p><p>01:19:30 Future of Mapping and AI in Transportation</p><p>01:29:14 Lessons for Early Career Professionals</p>","author_name":"Tejas Kumar"}