Posts

A guide to scaling AI models beyond the data pipeline—from training loops and distributed frameworks to 3D parallelism and fault tolerance.

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.

Pre-training gives models capability; post-training gives them value. A deep dive into LoRA, DoRA, DPO, and how we sculpt intelligence after the initial birth.

The unsung hero of modern data processing is how we structure data itself. Learn how Apache Parquet and Apache Arrow solve the fundamental trade-off between storage efficiency and compute speed.

How PagedAttention, Continuous Batching, Speculative Decoding, and Quantization unlock lightning-fast, reliable large language model serving.