The rise of autonomous driving technology promises to revolutionize transportation, offering safer, more efficient travel. However, the journey from concept to reality is fraught with challenges, particularly in the realm of Machine Learning Operations. This presentation will explore the five biggest MLOps challenges in autonomous driving: data management and quality, model training and validation, real-time inference and deployment, safety and reliability, and ethical and social implications. The talk provides valuable insights into the complexities of these challenges and uncovers strategies to overcome them, ensuring the successful deployment of AD systems.
In this session, you will learn more about:
- The importance of data collection, labeling, privacy, and security for autonomous driving
- Techniques for efficient training and validation, including CI/CD for continuous improvement
- Strategies to achieve low-latency decision-making and effective deployment on resource-constrained vehicles
- Implementing fail-safe mechanisms, to ensure regulatory compliance, and achieve explainable AI
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