Retraining

Deploying machine learning models in production Moving a model from a notebook to a live service is more than code. It requires planning for reliability, latency, and governance. In production, models face drift, outages, and changing usage. A clear plan helps teams deliver value without compromising safety or trust. Deployment strategies Real-time inference: expose predictions via REST or gRPC, run in containers, and scale with an orchestrator. Batch inference: generate updated results on a schedule when immediate responses are not needed. Edge deployment: run on device or on-prem to reduce latency or protect data. Model registry and feature store: track versions and the data used for features, so you can reproduce results later. Build a reliable pipeline Create a repeatable journey from training to serving. Use container images and a model registry, with automated tests for inputs, latency, and error handling. Include staging deployments to mimic production and catch issues before users notice them. Maintain clear versioning for data, code, and configurations. ...

Machine learning in production challenges and tips Bringing a model from a notebook to a live service is hard. Data shifts, user behavior changes, and limited resources create real risks. The goal is to keep good results while the world around the model keeps changing. Clear goals, good monitoring, and simple processes help teams stay in control. Common production challenges include data drift, model performance decay, and a growing gap between research work and daily operations. If monitoring is weak or alerts are noisy, small issues become outages or costly mistakes. Latency and costs can also block real-time use. Finally, governance and reproducibility matter: easy to reproduce experiments and roll back when needed. ...