Top 10 Open-Source Machine Learning Frameworks

10th Place: Metaflow

Developed by Netflix and available as an open-source project since 2019, this framework simplifies various challenges related to scaling and versioning ML projects. Metaflow stores code, data and dependencies in a content-addressed memory, making it possible to further develop workflows, reproduce old ones and edit new ones. By offloading individual steps to separate nodes on AWS, Metaflow makes it easier to scale the ML workflow without having to worry about communication between nodes. The framework is especially useful if you plan to manage and execute your ML workflow in a production environment on AWS.

9th Place: H2O

The ML platform H2O kills several birds with one stone: with various preconfigured algorithms such as GLM, XGBoost, Random Forest, DNN, GAM or K-means, as well as simple expansion options with Hadoop, Spark and other frameworks, the platform solves many difficulties of ML model development and provision. The intuitive UI and AutoML functionalities automate feature engineering, model training and model tuning for a faster workflow when creating new ML models. Because of its support for many common programming languages and useful MLOps functions, this platform is used by many large companies for use cases in the areas of insurance, finance and healthcare.
 

8th Place: Apache MXNet

This lean deep learning framework, developed by the Apache Software Foundation in partnership with Intel, is suitable for flexible prototyping and for use in the production environment. The framework was designed for the development of neural networks and impresses with a highly scalable infrastructure and a flexible model of neural networks in different programming languages. MXNet is ideal for parallelizing deep learning calculations on multiple CPUs or GPUs in the cloud – with almost linear scalability. The Gluon API as a frontend simplifies the use of the framework with many plug-and-play building blocks for developing neural networks, including predefined layers, optimizers, and initializers.
 

7th Place: Fast.ai

Developing an ML model prototype is time-consuming. As the name suggests, fast.ai makes it faster. The framework's high-level API offers preconfigured algorithms and a well-thought-out structure, thus ensuring faster development of functioning deep learning model prototypes. But that doesn't make the framework any less interesting for experts: the low-level API can be used to create sophisticated and finely tuned ML models and optimize them down to the smallest detail. The goal of the framework and the non-profit organization behind it is to “make neural networks uncool again”. This is not to belittle the popularity of neural networks, but to broaden the accessibility of the technology beyond the academic elite and experts.

6th Place: XGBoost

If you work with structured or tabular data, an algorithm based on decision trees should be on your shortlist. XGBoost offers the perfect combination of software and hardware optimization to accelerate the Gradient Boosted Trees algorithm. With APIs in Python, Java, C++, Scala and Julia, the framework supports multiple implementation options for Gradient Boosted Trees and runs on CPUs, GPUs and distributed computing resources. The framework has already impressed in many Kaggle competitions and, thanks to its speed, offers shorter calculation times than normal gradient boosting. The combination of hardware optimization, parallelization and cloud integration makes the framework ideal for accelerating calculations based on decision trees.

5th Place: Apache Spark

Spark offers you a complete solution for your ML workflow as one of the largest open-source projects in history. The unified computing engine for large-scale data analytics projects is one of the most actively developed open-source engines for machine learning and data processing. The Spark engine supports all common programming languages and can be easily combined with various frameworks and libraries. With Spark MLLib, the engine offers various algorithms and workflow tools for experimenting, deploying and scaling your ML model with the fast Spark computing engine. From SQL to data streaming to machine learning, Spark runs everywhere, from your laptop CPU to a server cluster, making it the right framework to start small and scale big.

4th Place: Scikit-Learn

For beginners who want to enter the world of ML with predictive data analysis, Scikit-Learn is the right choice. This robust and easy-to-understand framework offers a range of simple and efficient tools for data mining and data analytics. Because Scikit-Learn is based on various Python packages such as Numpy, SciPy and Matplotlib, the typical ML workflow is easy to understand and learn. Easy to use and with many well-described examples, Scikit-Learn is a great tool for beginners and new ML engineers who want to quickly achieve results with ML algorithms.

3rd Place: Keras

Developing neural networks can be more difficult than expected, especially for beginners. The Keras framework simplifies the definition and training of any type of neural network. Keras was originally developed for scientific purposes to reduce the time from idea to experiment with neural networks. The framework includes unified and simple APIs, reduces the number of steps required to develop neural networks for typical use cases, and provides user-friendly debugging. With the performance of TensorFlow and an API for rapid experimentation, Keras is an established framework used by scientists and many large companies.
 

2nd Place PyTorch

If you are looking for a flexible and highly scalable deep learning framework, you may have heard of PyTorch – and with good reason! One of the main advantages of using PyTorch for deep learning is PyTorch's so-called 'dynamic computation graphs'. While static graphs, like the one in TensorFlow, are defined before runtime, dynamic graphs define themselves as the model is calculated. In other words, the graph is rebuilt at each iteration. This can be particularly helpful when defining recurrent neural networks (RNN), for example in NLP use cases with variable input lengths. Because PyTorch is designed for a low-level environment, the framework offers a high degree of flexibility and many possibilities – but it can be a bit overwhelming for beginners.

1st Place: TensorFlow

The most popular deep learning framework is of course a must on this list. With APIs in Java, Python and JavaScript – but a core structure written in C++ – TensorFlow is fast but easy to use. The framework can be used to train and deploy deep neural networks, whether on servers, on the web or on edge devices. With countless industry-leading ML use cases, TensorFlow is one of the most versatile and popular frameworks for developing neural networks. What's more, the newly developed TensorFlow Extended even offers an end-to-end platform for developing and deploying complete ML pipelines.