Top machine learning libraries

Most ML libraries are made up of modules, allowing developers to mix and match as they build ML pipelines that handle pre-processing, training, validation metrics and other tasks. The libraries are frequently open-source and free to use, and there are many to choose from: one Github page aggregates nearly 1000 such ML libraries in the Python programming language alone. (Python has emerged as the dominant machine learning language—though ML projects also appear in JavaScript, R and other languages). 

There are libraries for all sorts of applications. Hugging Face’s transformers provide easy access to pretrained transformer models. Libraries such as Stable-Baselines3 support reinforcement learning. Machine learning libraries can be usefully clustered into two main categories. General libraries that serve as frameworks or platforms for machine learning projects. Specialized libraries can be used for a specific stage or component of an ML project.

General machine learning libraries—sometimes called “general-purpose frameworks” or “core platforms”—themselves number in the dozens. But four are particularly popular, routinely topping “best of” lists: TensorFlow (and the closely related Keras), PyTorch and scikit-learn. Each has slightly different strengths, depending on the needs of the project or team. 

• NumPy
  
  
• Tensorflow
  
  
• Keras
  
  
• PyTorch
  
  
• Scikit-learn

NumPy is not a ML library per se, but rather the library on whose shoulders all ML libraries are built. At its heart, machine learning is about finding patterns in large quantities of data. NumPy, a library which creates a structure known as an n-dimensional array, helps organize these datapoints and apply mathematical functions to them (a branch of math known as linear algebra). These n-dimensional or multidimensional arrays—again, big manipulable containers of numbers—are also sometimes called “tensors,” a frequently occurring term in discussions of ML libraries. (A 2-dimensional array is known as a matrix).

While NumPy handles tensors—the core data structure of machine learning—NumPy is in practice too limited for the processor-intensive demands of modern ML. Among other constraints, NumPy (whose roots trace to the 1990s) is too old to “talk” to the advanced graphics processing unit (GPU) processors that commercial ML efforts typically require (so-called “GPU acceleration”), instead only working with lower-horsepower central processing units (CPUs).

TensorFlow is a general ML library initially developed by the Google Brain team in 2015; after Google made the library open-source, it grew in popularity. TensorFlow can work not only with CPU processors, but also high-performance GPUs and a specialized Google-made processors called a tensor processing unit (TPU).

TensorFlow is particularly well suited to deep learning, a variant of machine learning that relies on neural networks (which imitate the structure of the brain). “Deep” learning is so called because it involves multiple layers between and input and an output. Deep learning has emerged as useful in commercial applications like natural language processing (NLP), computer vision and image recognition. Originating at Google and powering many of its commercial apps and products, TensorFlow excels at large-scale deployment. 

Keras is closely associated with TensorFlow; also created by a Google engineer. It is a library that is typically used by developers wanting a more user-friendly API for their TensorFlow-based ML projects. A version of Keras released in 2025 added support for other frameworks beyond TensorFlow, including PyTorch. Keras is also renowned for its extensive documentation and helpful tutorials.

PyTorch was originally developed by researchers at Meta in late 2016. It’s a Python port of the older Torch library, at whose core was a tensor. By 2022, at which point PyTorch moved to the Linux Foundation, over 2,400 contributors had reportedly over 150,000 projects using PyTorch. (Open-source machine learning is the dominant paradigm, since the field flourishes from extensive collaboration.) Like TensorFlow, PyTorch similarly allows developers to perform NumPy-like operations, but using GPUs instead of CPUs—making PyTorch another deep learning framework. 

“PyTorch or TensorFlow?” is often an initial question for those embarking on a machine learning effort (Formerly, a library called Theano was also in the mix; it was deprecated in 2017). While there is no wrong answer, PyTorch is emerging as a favorite with many developers for its flexible and forgiving (“Pythonic”) design and ease of use. Long favored among academics and researchers, industry increasingly uses it for ambitious, scalable use cases as well. Tesla’s Autopilot, for instance, was built using PyTorch, and Microsoft’s cloud computing platform Azure supports it. PyTorch has become so popular that an ecosystem of supporting tools (like Torchvision and TorchText) has grown around it. Both Tensorflow and Pytorch use a computational graph—a data structure that represents the flow of operations and variables during model training. 

IBM is a member of the PyTorch Foundation; it uses PyTorch with its watsonx portfolio.

Scikit-learn (styled lower-case “scikit-learn,” and also known as “sklearn”) is another foundational ML library, designed to interoperate with NumPy and a related library popular with data scientists called SciPy, which supports scientific computing. Scikit-learn includes a number of ML algorithms whose essence is pattern recognition. For instance, it includes classification algorithms (like those that judge whether an email is spam or not), regression algorithms (which support prediction, forecasting and recommendation systems) and clustering algorithms (which group similar items together). While scikit-learn is a great place for beginners to learn the basics of machine learning—concepts like data pre-processing, data pipelines, decision trees and optimization—it is limited as an engine for the making of commercial products. Like NumPy, scikit-learn lacks GPU acceleration, meaning it is not suitable for deep learning models and is not considered a “deep learning library.” Nevertheless, it is still useful as a laboratory for testing ideas and prototyping.

The core of any ML model—in essence, the learning part—will run on one of the foundational libraries listed above. But machine learning is a complex, multi-stage endeavor, and so libraries have evolved to help with the workflows pertaining to specific ML tasks. Additionally, different industries (like the financial or medical fields) and different data types (like images or audio data) are sufficiently distinct to benefit from dedicated ML libraries. While it’s beyond the scope of this article to examine the nearly thousand of open-source libraries resulting from this complexity, it is helpful to illustrate just a few particularly popular ones.

Pandas is the premier Python library for data science, a core function in any ML effort; like so many ML libraries, it is built on top of NumPy. Pandas goes further than NumPy’s arrays by adding a structure known as a “data frame,” which is similar to an Excel spreadsheet. This added structure makes it possible to perform data manipulation on large datasets of real-world data.

For the purposes of revealing patterns and insights from visual data, two popular data visualization libraries are matplotlib and seaborn. The former produces plots and graphs, the latter sits on top to make it a bit more ML-friendly (seaborn, for instance, can work directly with pandas’ data frames).

Launching a viable machine learning effort requires a lot of experimentation and trial-and-error to get right. To that end, the library MLFlow helps teams log ML models, parameters and results, as well as manage debugging efforts, helping move trained models into something ready to ship.