The journey from identifying data-related issues in perception models to effective ML training is crucial for next-generation autonomy programs. Data-driven workflows provide a robust framework that starts by triaging current model challenges, particularly when faced with insufficient training data. These innovative workflows streamline the collection and generation of diverse, high-quality datasets within a single platform, to help improve model performance through a combination of real and synthetic data.
In this session, you will learn how:
- The transition from triaging to ML training is essential for addressing data limitations in next-gen autonomy
- Data-driven workflows begin by identifying and triaging issues within perception models linked to data insufficiencies
- It enables efficient collection and creation of high-quality datasets, filling gaps to improve training
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