{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Provides an HTTP server for serving Azure Machine Learning models with built-in support for Flask-based scoring scripts, OpenTelemetry observability, and Azure Monitor integration.","skillfed_tags":["azure-ml","model-serving","observability"],"use_cases":["Deploy a trained scikit-learn or TensorFlow model as a production HTTP endpoint in Azure ML with automatic request validation and response formatting.","Monitor inference latency and error rates in production by leveraging built-in OpenTelemetry metrics and Azure Monitor export.","Host a batch of related scoring functions (e.g., preprocessing, model inference, postprocessing) as separate endpoints on a single server instance.","Migrate existing Flask-based scoring scripts to Azure ML without rewriting the core logic, using the server's Flask compatibility layer.","Configure separate health check and inference ports to isolate monitoring traffic from production scoring requests."],"what_it_does":"Azure Machine Learning Inference HTTP Server is a Flask-based HTTP server designed to host and serve machine learning models deployed through Azure ML. It wraps user-defined scoring scripts (Python functions decorated with input/output schemas) and exposes them as HTTP endpoints, handling request routing, response formatting, and error management. The server includes built-in observability through OpenTelemetry and Azure Monitor integration, allowing operators to collect metrics, traces, and logs for production monitoring.\n\nThe package abstracts away much of the HTTP server boilerplate, letting data scientists focus on the scoring logic while the server handles Flask configuration, CORS, health checks, and request/response validation. It supports both gunicorn (Linux/macOS) and waitress (Windows) as WSGI servers and includes features like configurable health check ports and JSON-based server configuration through environment variables.","worth_installing":"Yes, if you are deploying models through Azure ML and need a standardized, Microsoft-supported HTTP server for inference. The package is actively maintained with recent dependency upgrades (Pydantic 2.12 in version 1.5.1) and has no known security vulnerabilities. The aging maintenance status reflects a stable, mature codebase rather than abandonment. However, review the unclear proprietary license terms before use, and ensure your scoring scripts are compatible with Flask 2.x and Pydantic 2.0+."},"id":"azureml-inference-server-http","links":{"html":"https://skillfed.io/packages/azureml-inference-server-http","md":"https://skillfed.io/packages/azureml-inference-server-http.md","pypi":"https://pypi.org/project/azureml-inference-server-http/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-12-18","license_spdx":null,"license_treatment":"unclear","name":"azureml-inference-server-http","python_support":"supports_current","summary":"Azure Machine Learning inferencing server."},"popularity":{"monthly_downloads":181485,"position":10126,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.1"}