Zachary Carrico

As video foundation models become integral to applications in healthcare, security, retail, robotics, and consumer applications, MLOps teams face a new class of challenges: how to efficiently fine-tune these large models for domain-specific tasks without overcomplicating infrastructure, overloading compute resources, or degrading real-time performance.

Interpretability is crucial for improving model performance, reducing biases, and ensuring compliance with AI safety and fairness regulations. In this session, complex neural networks will be transformed from opaque “black boxes” into interpretable “glass boxes” by exploring a wide range of neural network-specific interpretability techniques.