Jan Zawadzki
How Machine Learning is turning the Automotive Industry upside down
#1about 4 minutes
The automotive industry's massive economic and social impact
The automotive industry is a major global economic driver with significant market capitalization and employment, but it faces stagnating car sales despite growing mobility demands.
#2about 4 minutes
How machine learning creates value from automotive data
Machine learning finds patterns in vast amounts of car data to create a virtuous cycle of better products and more users, similar to how the internet industry evolved.
#3about 4 minutes
Applying machine learning to automated driving and personalization
Machine learning improves automated driving by reducing costs and time while increasing quality, and it also enhances the in-car experience through personalization.
#4about 3 minutes
Challenge one: Managing massive data volumes and high sensor costs
A key challenge is managing the terabytes of data generated by modern cars daily and integrating expensive sensors without destroying thin profit margins.
#5about 4 minutes
Challenge two: Adapting legacy architectures and processes
The automotive industry must shift from complex legacy architectures and waterfall development to agile, data-driven processes that support the entire ML lifecycle.
#6about 5 minutes
Challenge three: Ensuring machine learning models are robust
ML models can learn incorrect correlations from data, as shown by husky/wolf and criminal-detection examples, highlighting the need for better interpretability for safe deployment.
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Matching moments
02:06 MIN
Applying machine learning in the automotive industry
Getting Started with Machine Learning
04:48 MIN
Overview of the data-driven development lifecycle for cars
Developing an AI.SDK
06:13 MIN
Skills and challenges of working with automotive AI
Developing an AI.SDK
06:47 MIN
Adapting DevOps principles for automotive and IoT systems
A solution to embed container technologies into automotive environments
07:29 MIN
Q&A on automotive technologies and legal liability
The future of automotive mobility: Upcoming E/E architectures, V2X and its challenges
03:01 MIN
Common challenges in developing machine learning applications
Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization
01:57 MIN
The automotive industry's shift to software-defined vehicles
Cybersecurity for Software Defined Vehicles
04:56 MIN
Adapting the traditional V-model for ML development
What non-automotive Machine Learning projects can learn from automotive Machine Learning projects
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