Topics

Large language models: architectures, capabilities, and how they are built.

Agentic systems, tool use, and autonomous workflows.

Serving, latency, throughput, and running models in production.

Pretraining, fine-tuning, and the methods behind model training.

GPUs, accelerators, and the silicon ML runs on.

Alignment, evaluation, reliability, and trustworthy ML.

Vision, audio, video, and generative models beyond text.

Frameworks, libraries, and the developer ecosystem.