The Infrastructure Round
Scaling, serving, and optimizing AI systems. Custom kernels, inference engines, and production infrastructure.
7 articles covering mlops & production
The journey from stateless inference to stateful, tool-augmented AI agents requires a complete reimagining of MLOps infrastructure. Learn how the Model Context Protocol (MCP), secure sandbox environments, distributed tracing, and holistic versioning enable the next generation of agentic AI systems.
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.
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 in large-scale analytics and ML pipelines.
Why modern AI teams are handcrafting GPU kernels—from FlashAttention to TPU Pallas code—and how smarter tooling is making silicon-level tuning accessible.
How PagedAttention, Continuous Batching, Speculative Decoding, and Quantization unlock lightning-fast, reliable large language model serving.
A reader-friendly guide to scaling AI models beyond the data pipeline—from training loops and distributed frameworks to checkpoints, mixed precision, and fault tolerance.
A deep dive into how datasets and dataloaders power modern AI—from the quiet pipeline that feeds models to the sophisticated tools that make training efficient. Understanding the hidden engine that keeps AI systems running.