Top 5 Machine Learning Frameworks in 2024
All frameworks or projects are ranked by a project-quality score, which is calculated based on various metrics for ranking collected from GitHub and different package managers.
- Tensorflow
TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications.
TensorFlow was originally developed by researchers and engineers working within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. However, the framework is versatile enough to be used in other areas as well.
TensorFlow provides stable Python and C++ APIs, as well as a non-guaranteed backward-compatible API for other languages.
2. PyTorch
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
3. Scikit-Learn
scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.
The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.
It is currently maintained by a team of volunteers.
Website: https://scikit-learn.org
4. JAX
JAX is Autograd and XLA, brought together for high-performance numerical computing, including large-scale machine learning research.
With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.
What’s new is that JAX uses XLA to compile and run your NumPy programs on GPUs and TPUs. Compilation happens under the hood by default, with library calls getting just-in-time compiled and executed. But JAX also lets you just-in-time compile your own Python functions into XLA-optimized kernels using a one-function API, jit. Compilation and automatic differentiation can be composed arbitrarily, so you can express sophisticated algorithms and get maximal performance without leaving Python. You can even program multiple GPUs or TPU cores at once using pmap, and differentiate through the whole thing.
Dig a little deeper, and you’ll see that JAX is really an extensible system for composable function transformations. Both grad and jit are instances of such transformations. Others are vmap for automatic vectorization and pmap for single-program multiple-data (SPMD) parallel programming of multiple accelerators, with more to come.
5. Keras
The purpose of Keras is to give an unfair advantage to any developer looking to ship Machine Learning-powered apps. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on. Your models run faster thanks to XLA compilation with JAX and TensorFlow, and are easier to deploy across every surface (server, mobile, browser, embedded) thanks to the serving components from the TensorFlow and PyTorch ecosystems, such as TF Serving, TorchServe, TF Lite, TF.js, and more.
Keras is the high-level API of the TensorFlow platform. It provides an approachable, highly-productive interface for solving machine learning (ML) problems, with a focus on modern deep learning. Keras covers every step of the machine learning workflow, from data processing to hyperparameter tuning to deployment. It was developed with a focus on enabling fast experimentation.
With Keras, you have full access to the scalability and cross-platform capabilities of TensorFlow. You can run Keras on a TPU Pod or large clusters of GPUs, and you can export Keras models to run in the browser or on mobile devices. You can also serve Keras models via a web API.
Keras is designed to reduce cognitive load by achieving the following goals:
- Offer simple, consistent interfaces.
- Minimize the number of actions required for common use cases.
- Provide clear, actionable error messages.
- Follow the principle of progressive disclosure of complexity: It’s easy to get started, and you can complete advanced workflows by learning as you go.
- Help you write concise, readable code.
Below are some more frameworks that I’ve used or read about them.
Thank you for reading this blog, Clap 👏 if you worked on any of the above frameworks mentioned above.
