The five best machine learning frameworks to build on in 2026 are scikit-learn, XGBoost, PyTorch, TensorFlow with Keras, and ML.NET. They suit different problems, so the order below runs from the one a typical business is most likely to need to the most specialised. A machine learning framework is a library of code for training a model: a program that learns a pattern from past examples and applies it to new ones. Before choosing one, it is worth asking whether you need to train a model at all, a question covered near the end.
The version numbers were checked against each project’s own site or published packages on 2 October 2026.
How the five were chosen
Each framework on the list:
- has had a stable release within the last twelve months;
- is open source and free to use commercially;
- has an identifiable organisation or established community behind it;
- does a job the others on the list do not.
Two frameworks from the earlier version of this article, Apache MXNet and Microsoft Cognitive Toolkit, have been retired and are discussed below.
The five at a glance
| Framework | Current version | Best at | Behind it |
|---|---|---|---|
| scikit-learn | 1.9 | Predictions from tables of business data | Open-source community |
| XGBoost | 3.4 | The same, where accuracy matters most | Open-source community |
| PyTorch | 2.14 | Deep learning: images, text, speech | PyTorch Foundation |
| TensorFlow with Keras | 2.21 and 3.15 | Deep learning, especially on phones and devices | |
| ML.NET | 5.0 | Machine learning inside .NET applications | Microsoft |
The five in more detail
scikit-learn
Most business data lives in tables: customers, orders, invoices, jobs. scikit-learn is the standard Python library for learning from data of that shape. It covers classification (which category does this belong to), regression (what number should we expect), clustering (which records resemble each other) and the preparation steps around them. It is released under the BSD licence, and version 1.9 came out in June 2026.
It is the right starting point for questions such as which customers are likely to leave, which invoices are likely to be paid late or how many orders to expect next month. Models train in seconds or minutes on an ordinary computer, and many of them can show which factors drove a prediction, which matters when someone asks why.
XGBoost
XGBoost implements one technique, gradient boosting, which builds a prediction from many small decision trees. On tabular data, gradient boosting is frequently among the most accurate methods available, and XGBoost is usually used alongside scikit-learn, not in place of it. Version 3.4 was released in August 2026. Reach for it when a scikit-learn model works but a few points of accuracy are worth real money.
PyTorch
PyTorch is the leading framework for deep learning, the family of techniques behind image recognition, speech recognition and language models. It began at Meta and is now governed by the PyTorch Foundation, which is hosted by the Linux Foundation. Version 2.14 was released in September 2026. In practice, most new research and most openly published models appear for PyTorch first.
A typical business has little reason to train a deep learning model from nothing. The realistic use is to take a published model and adapt it to your own images or documents, which is called fine-tuning.
TensorFlow with Keras
TensorFlow is Google’s deep learning framework, and Keras is the simpler interface most people use to drive it. Since version 3, Keras can also run on top of PyTorch or JAX, another Google library, so code written in Keras is not tied to TensorFlow. TensorFlow’s current release is 2.21, from March 2026, and Keras is at 3.15.
TensorFlow is still maintained and still widely deployed, but its releases now come less often than PyTorch’s. It has long been used to run models on phones, in browsers and on small devices, and a large amount of existing production code is written for it. For a new project with no such constraint, our view is that PyTorch is now the more usual starting point.
ML.NET
ML.NET is Microsoft’s open-source framework for building machine learning into applications written in C#. It handles the same tabular problems as scikit-learn, and it can load models trained elsewhere, including TensorFlow models and models saved in ONNX, an open file format for exchanging models between frameworks. Version 5.0 was released in November 2025.
Its advantage is practical. If your business system is a .NET application, a model built or loaded with ML.NET runs inside it, maintained by the same developers with the same tools. No separate Python service has to be hosted and kept in step. The Python frameworks have far more examples, tutorials and practitioners, so a common pattern is to explore and train in Python, export the finished model to ONNX and run it from the .NET application.
What happened to MXNet and CNTK
Both were in this article when it was first published, and both are now retired.
- Apache MXNet, which AWS contributed to heavily, was retired by the Apache Software Foundation in September 2023 and moved to the Apache Attic, its home for discontinued projects.
- Microsoft Cognitive Toolkit (CNTK) is no longer developed. Its code repository is archived and read-only.
The lesson for a buyer is that backing from a large company does not guarantee a long life. A model built on a retired framework still runs, but the framework stops receiving security fixes, stops supporting new versions of Python and new hardware, and becomes steadily harder to find anyone to work on. Keeping a copy of each model in ONNX format where it can be exported, along with the training data and the code that prepared it, is cheap insurance.
When you do not need a framework at all
Often you do not. Much of what businesses now call AI does not involve training anything. Reading invoices, summarising emails, answering questions from your own documents and drafting replies are done by calling a large language model that a provider hosts, and paying by usage. We describe that kind of work under AI for existing systems, and compare the main hosts in our comparison of machine learning as a service providers.
A framework earns its place when all of these are true:
- you want a number or a category predicted, not text written;
- you hold a good history of past cases with known outcomes;
- the prediction will be made often enough to justify the work;
- someone will act on it.
Our article on the business benefits of machine learning goes through where that tends to pay off.
What the choice means if you are paying for it
Skills. All but ML.NET are Python libraries. If your in-house developers work in C#, a Python model is something they will need help to maintain. Decide early who looks after it once the person who built it has moved on.
The model is the small part. In our experience the framework accounts for little of the effort. Getting clean data out of your systems, and getting the prediction back in front of the person who needs it, is most of the work. That is ordinary system integration.
Models go stale. A model reflects the period it was trained on. Budget for checking its accuracy regularly and retraining it.
Three questions to put to anyone proposing a machine learning project: which framework and version will it use, where will the model run, and how will we know in a year whether it is still accurate?