AutoML vs Custom Models: When to Use Each
A decision framework for choosing between AutoML platforms and hand-built models — covering cost, control, accuracy, and the trade-offs that actually matter in production.
10 guides tagged “mlops”.
A decision framework for choosing between AutoML platforms and hand-built models — covering cost, control, accuracy, and the trade-offs that actually matter in production.
How to automate the repetitive parts of the ML lifecycle — retraining, evaluation, and inference pipelines — using tools developers already know.
A real, honest production architecture: an ONNX image classifier served by FastAPI on Railway, loaded from object storage at startup, with one shared inference session on CPU — and what we'd improve.
The core trade-off in ML deployment: run inference on a central server or push it to the device. Real case: why TrichAi chose the server, what it cost, and when you'd choose differently.
A practical guide to Dockerizing a FastAPI inference service: a lean multi-stage build, why you shouldn't bake the model into the image, sane defaults for uvicorn, and the mistakes that bloat images.
Models change more often than code, and a bad model can be worse than a bug. A practical guide to versioning model artifacts and rolling back fast when a new model underperforms.
Processing requests one at a time wastes hardware; batching them trades a little latency for a lot of throughput. How dynamic batching works, when it helps, and when a single shared session is enough.
A model that passed every test can still rot in production as the world changes. What to monitor for an ML service — latency, data drift, prediction drift — and how to start when you have zero instrumentation today.
An ML endpoint that accepts file uploads is an attack surface. Practical hardening for inference APIs — input validation, size and rate limits, and the defenses that matter before you take traffic.
When your service loads a model from object storage at boot, cold starts get slow. Why ML services start slowly, and the practical levers — image size, lazy loading, warm instances, and model size — to fix it.