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Framework Release Watch No4
Angular 22.1.0 expands signals, CSS isolation, and HTTP caching • FastAPI 0.141.0 streamlines frontend development • Quarkus 3.38.0 improves proxy and Gradle handling
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From pickle to production: wrapping a model in a real API that won’t fall over
Training a machine learning model is only half the job. The other half is making it available reliably, safely, and predictably.
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Using FastAPI to build a mini REST API in Python
FastAPI has rapidly become one of the go-to frameworks for building high-performance REST APIs in Python. In this guide, we will walk through creating a mini REST API from scratch using FastAPI, explore how it compares with Flask and Django REST Framework, and learn practical tips for testing, documentation, and deployment.
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Tools: FastAPI, Docker, BentoML
FastAPI, Docker, and BentoML together form a powerful, production-grade stack for deploying machine learning models. This post explores how each tool fits into the MLOps pipeline, how to integrate them efficiently, and which best practices high-performing teams are using in 2025 to deploy models at scale.