Introducing Flama for Robust Machine Learning APIs

• flama

  4 254 6.3 Python

  Fire up your models with the flame 🔥

  

There has been a considerable effort in the last few years to try and standardise the way in which these type of APIs are implemented via different frameworks. However, over the last few years, a new type of functionality has become more and more popular: machine learning (ML) models; and the existing frameworks for building APIs are not well suited for this type of functionality. In this series of posts, we will learn how to build APIs using a Framework for the development of Lightweight Applications and Machine-learning Automation, also known as Flama.

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• pyenv

  262 37,438 8.8 Roff

  Simple Python version management

  

When dealing with software development, reproducibility is key. This is why we encourage you to use Python virtual environments to set up an isolated environment for your project. Virtual environments allow the isolation of dependencies, which plays a crucial role to avoid breaking compatibility between different projects. We cannot cover all the details about virtual environments in this post, but we encourage you to learn more about venv, pyenv or conda for a better understanding on how to create and manage virtual environments.

• flama-demos

  1 1 1.8 Python

  Examples showcasing Flama 🔥

  

The goal of this post is not to build a very complex ML model by itself, but we want to go further than the prototypical Iris classification problem. For this reason, we are going to use a dataset which is a bit more complex, but still simple enough to be able to focus on the ML pipeline and the ML-API. The problem we are going to address has to do with the prediction of customer churn, i.e. the prediction of whether a customer will leave a company or not, which is a very common problem in the industry. The dataset we are going to use is a public dataset, which you can download from here. For the sake of brevity, we are not going to discuss here the details of the dataset, we will just assume that the data exploration has already been done.

• SQLAlchemy

  129 9,025 9.7 Python

  The Database Toolkit for Python

  

Besides, flama also provides support for SQL databases via SQLAlchemy, an SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. Finally, flama also provides support for HTTP clients to perform requests via httpx, a next generation HTTP client for Python.

• httpx

  54 12,566 8.8 Python

  A next generation HTTP client for Python. 🦋

  

Besides, flama also provides support for SQL databases via SQLAlchemy, an SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. Finally, flama also provides support for HTTP clients to perform requests via httpx, a next generation HTTP client for Python.

• Pytorch

  349 79,328 10.0 Python

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  

PyTorch

• Poetry

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  Python packaging and dependency management made easy

  

We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.

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• warehouse

  278 3,492 9.7 Python

  The Python Package Index

  

We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.

• Pandas

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  Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

  

pandas: A library for data analysis in Python

• NumPy

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  The fundamental package for scientific computing with Python.

  

numpy: A library for scientific computing in Python

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