AI, Machine Learning and Computer Vision Meetup
Oct 10, 2024 at 10 AM Pacific
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How Renault Leveraged Machine Learning to Scale Electric Vehicle Sales
Vincent Vandenbussche
Sr. ML Engineer / Author
In 2019, Renault sought a scalable solution to estimate the cost of home charging station installations for electric vehicle buyers. A machine learning solution using satellite images and a shortest-path algorithm was developed to automate this process. Despite challenges, the optimized solution was deployed as a cloud-based API, enabling Renault to scale their EV sales from 50,000 in 2019 to over 220,000 in 2022.
About the Speaker
With a PhD in Physics, Vincent Vandenbussche has over a decade of experience deploying scalable machine learning solutions for leading companies like Renault and Chanel. He is also passionate about sharing his expertise through Medium posts and his book, The Regularization Cookbook.
RGB-X Model Development: Exploring Four Channel ML Workflows
Dan Gural
Machine learning Engineer / Developer Evangelist
Machine Learning is rapidly becoming multimodal. With many models in Computer Vision expanding to areas like vision and 3D, one area that has also quietly been advancing rapidly is RGB-X data, such as infrared, depth, or normals. In this talk we will cover some of the leading models in this exploding field of Visual AI and show some best practices on how to work with these complex data formats!
About the Speaker
Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.
Elasticsearch is for the Birds: Identifying Feathered Friend Embedding Images as Vector Similarity Search
Justin Castilla
Elasticsearch
Search no longer has to be a traditional term / frequency-inverse document frequency slog. The current trend of machine learning and models has opened another dimension for search, quite literally. In this talk we’ll cover “classic” search and its inherent limitations, what a model is and how you can use it.
Next, we’ll look at how to perform vector search or hybrid search using images of birds in Elasticsearch, generate embeddings from images and then use the techniques we learned to propose the most probable similar images after uploading our own image. We’ll close the talk with an overview of various enhancements to increase the performance and usability of your searches.
About the Speaker
Justin Castilla is a Senior Developer Advocate at Elastic based in Seattle. His main focus is education and developer empowerment, and enjoys sharing knowledge and learning experiences with everyone.
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