The goal of this repository is to enable data scientists and ML engineers to develop data science use cases and making it ready for production use. This means focusing on the versioning, scalability, monitoring and engineering of the solution.

IBM Last update: Apr 04, 2024

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DRF-like declarative viewsets for FastAPI

Core functionality only (basic viewsets & schema classes): Database tools: Let's create a viewset for managing content posts. It will provide full kit of actions with simple mock data: Pseudocode…

Python ticketing utility for working with tickets in popular tools

ticketutil is a Python module that allows you to easily interact with various ticketing tools using their REST APIs. Currently, the supported tools are JIRA, RT, Redmine, Bugzilla, and ServiceNow. Al…

HUB REST API Python bindings

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:evergreen_tree: Python module for communicating with the Taiga API

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IDEX v3 Exchange REST API python implementation

The examples above use the wallet private key when creating the Client to specify which wallet to interact with. IDEX v3 supports the Mumbai testnet as a sandbox to test functionality. For other toke…

Serving a keras model (neural networks) in a website with the python Django-REST framework.

Python library to interface with Gerrit's REST API

Pygerrit2 is tested on the following platforms and Python versions: Support for Python 2.x is no longer guaranteed. To install pygerrit2, simply: This simple example shows how to get the user's open …

Code repository for Building RESTful Python Web Services, published by Packt

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02. The code will look like the following: In order to work with the …

⛔️ [DEPRECATED] python wrapper for Firebase's REST API

A very simple wrapper for Firebase's REST API. Install python-firebase using pip: or with easy_install (not recommended): Then simply import firebase at the top of your python script: and then instan…

Quickly turn command-line applications into RESTful webservices with a web-application front-end. You provide a specification of your command line application, its input, output and parameters, and CLAM wraps around your application to form a fully fledged RESTful webservice.

CLAM allows you to quickly and transparently transform your Natural Language Processing application into a RESTful webservice, with which both human end-users as well as automated clients can interac…