A deeper understanding of the importance of ML training data diversity for AV systems.

Knowledge about leveraging synthetic datasets and simulations effectively.

Insights into strategies for sourcing, labeling diverse datasets while managing time and costs.

Supervised, unsupervised ML training paradigms and their landscape over the course of next 5 years.

Specialized labeling, common pitfalls, and best practices for managing large data volumes.

Industry trends and Innovations in the data collection, mining, and annotations world.