Getting Started with ONNX: Train and Deploy Custom Models
A practical, end-to-end guide to ONNX: what it is, how to export models from PyTorch and TensorFlow, run fast inference with ONNX Runtime, and ship to production.
machine-learning
8 guides tagged “machine-learning”.
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.
ML Automation for Developers: AI Workflows That Work
How to automate the repetitive parts of the ML lifecycle — retraining, evaluation, and inference pipelines — using tools developers already know.
Server-Side vs On-Device ML Inference: How to Choose
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.
The Silent Accuracy Killer: Preprocessing Mismatch
Your model scores 94% in the notebook and falls apart in production. The cause is usually not the model — it's a preprocessing mismatch between training and inference. Here's how to find and prevent it.
Choosing a Model Format: ONNX vs TorchScript vs SavedModel
Once a model is trained, how you serialize it shapes everything downstream. A practical comparison of ONNX, TorchScript, and TensorFlow SavedModel — portability, performance, and lock-in.
Model Versioning and Rollback for ML Services
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.
Monitoring ML Models in Production: Drift, Logging, and Alerts
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.