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Tag: FastAPI

  • Framework Release Watch No22

    FastAPI 0.157.0 Combines Request Parameter Models • FastAPI 0.158.0 Adds Combined Form Models • FastAPI 0.159.0 Supports Optional Parameter Models • FastAPI 0.160.0 Changes Extra-Field Validation • FastAPI 0.161.0 Combines Unions of Form Models…

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  • Framework Release Watch No21

    FastAPI 0.146.0 Deprecates root_path Configuration • FastAPI 0.153.0 Unifies Browser Form Handling • FastAPI 0.152.0 Improves Header Validation Errors • FastAPI 0.151.0 Changes JSON Null Validation • FastAPI 0.150.0 Refines File Upload Validation…

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  • Framework Release Watch No20

    FastAPI 0.143.0 Makes OpenTelemetry Auto-Configuration Opt-In • Laravel 12.70.0 Improves Parameter Handling and Cache Isolation • Laravel 13.36.0 Adds Cloud IAM Storage Support • Express 5.3.0 Tightens HTTP Response Handling • Node.js 24.22.0 Expands Cryptography and Performance APIs…

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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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  • Framework Release Watch No2

    Laravel 13.22.0 Expands Queues, HTTP Fakes, and Container Binding • FastAPI 0.140.0 Cuts Dependency Memory Usage

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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.

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