ML Ops

Hey,

Hope you're keeping well.

If your ML model needs low-latency, real-time inference, expose it via an API using ASP.NET Core or Azure Functions, backed by a containerized deployment in Azure Kubernetes Service or Azure App Service. For large-scale, periodic processing, implement batch jobs using Azure Data Factory, Azure Batch, or scheduled .NET worker services that pull data, run inference, and persist results. In both cases, follow MLOps best practices—version models with MLflow or Azure Machine Learning, automate CI/CD with Azure DevOps pipelines, and monitor performance and drift using Application Insights or AML monitoring hooks—so deployment and retraining are consistent and reproducible.

Thanks and regards,

Taz