Introducing Flama for Robust Machine Learning APIs

1. flama

   1 8 276 8.7 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.

2. Stream

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3. pyenv

   2 275 42,513 9.1 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.

4. flama-demos

   3 1 2 4.1 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.

5. SQLAlchemy

   4 139 10,613 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.

6. httpx

   5 61 14,285 7.7 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.

7. Pytorch

   6 393 91,347 10.0 Python

   Tensors and Dynamic neural networks in Python with strong GPU acceleration

   

   PyTorch

8. Poetry

   7 409 33,409 9.5 Python

   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.

9. InfluxDB

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10. warehouse

    8 299 3,757 9.8 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.

11. Pandas

    9 427 45,889 9.9 Python

    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

12. NumPy

    10 303 29,886 10.0 Python

    The fundamental package for scientific computing with Python.

    

    numpy: A library for scientific computing in Python

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