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How to accelerate diffusion sampling and steer creativity. Learn the mechanics of DDIM, DPM-Solver, Classifier-Free Guidance (CFG), and the math of negative prompting.
The fundamentals of video diffusion models. Learn how we extend 2D diffusion to time, the mechanics of temporal attention, and the architectural shifts required for motion consistency.
From stateless inference to tool-augmented AI agents. Learn how the Model Context Protocol (MCP), secure sandboxes, and holistic versioning enable the next generation of AI systems.
Why modern AI teams are handcrafting GPU kernels—from FlashAttention to Triton code—and how silicon-level tuning is the new frontier of MLOps.
Standard MLOps advice tells you to learn Git and Docker. But for the next generation of AI Engineers, that's just the baseline. This roadmap focuses on the Infrastructure Round—deep-diving into how data is structured for speed, how it's fed into models, how those models scale across clusters, and how we squeeze every drop of performance out of the silicon.
A comprehensive deep-dive into production inference optimization, tracing the path of a request through LLM and diffusion model serving systems. Understanding the bottlenecks from gateway to GPU kernel execution.