Serving

AI in Practice: Deploying Models in Production Environments Bringing a model from research to real use is a team effort. In production, you need reliable systems, fast responses, and safe behavior. This guide shares practical steps and common patterns that teams use every day to deploy models and keep them working well over time. Plan for production readiness Define input and output contracts so data arrives in the expected shape. Freeze data schemas and feature definitions to avoid surprises. Version models and features together, with clear rollback options. Use containerized environments and repeatable pipelines. Create a simple rollback plan and alert when things go wrong. Deployment strategies to consider ...

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. ...