Most feature pipelines are just Python scripts and CSV files until they break in production. This post covers using Feast for feature management and Ray for distributed compute to fix training-serving skew and cut latency.
Agentic RAG setups waste time and money answering the same questions repeatedly. This post covers a two-tier semantic and retrieval cache, plus the validation logic needed to keep it from serving stale answers.
Most AI features never leave the notebook. Notes on the production data architecture that gets them into a live site: separated layers, database integrity over raw insert speed, and the state and permission problems agents bring with them.
Sharpness-Aware Minimization finds flatter minima instead of chasing zero training loss, though it doubles training cost and can quietly break your BatchNorm stats if you’re not careful.
GPU-to-GPU communication, not raw TFLOPS, is what actually limits AI cluster scaling. This post breaks down PCIe, NVLink, and NVSwitch, and why the performance cliff hits once you scale past a single node.
A practical way to stop babysitting deep learning runs: containerize your training script, add a health-check sidecar, and let an agent handle restarts and hyperparameter tweaks.
Layoffs dominate the headlines, but the data tells a different story: senior hiring keeps growing while roles fragment into analyst, ML engineer, and infrastructure tracks.
Gemini’s open-vocabulary detection removes the label-thousands-of-images step from a vision pipeline. Notes on structured JSON output with Pydantic, editing detected regions with Nano Banana, and the rate limits that show up in production.
Getting a larger effective batch size out of the GPUs you already have: gradient accumulation in a PyTorch loop, DDP with no_sync(), why the loss has to be scaled, and the bucket size and data loading settings that decide whether scaling stays linear.
Round-robin lead assignment ignores capacity, licensing and expertise, and a transient counter invites race conditions. A look at running the match as a linear program in PuLP outside WordPress, split into a batch mode and an online mode.