Top 10 Machine Learning Frameworks in 2022

Evaluating a machine learning library for commercial deployment can mean choosing between the latest trends and the most reliable and proven architectures. By the time an ML library has gained market traction and widespread developer availability, it's often struggling to assimilate the most recent innovations in the machine learning sector.

Conversely, more recent frameworks may have only a nascent user and developer base, with roadmaps that depend upon an industry take-up and economy of scale that may never arrive.

The domain and scope of our project should be the primary deciding factor along with commercial licensing costs. In some cases, where goals are very predefined, a mature legacy architecture may be a better solution than the current 'buzz' technologies.

Therefore, here is the comparison of ten notable machine learning frameworks covering the most popular fields in AI consulting, featuring a diversity of maturity levels and rates of industry adoption.

Sectors: Classification and filtering

Language: Scala

License: Apache License 2.0

MLlib is a dedicated machine learning Library for the low-latency Spark framework. MLlib facilitates supervised and unsupervised training, and benefits from Spark's RAM-driven performance boost. Machine learning algorithms supported are: regression, collaborative filtering, clustering, and binary classification.

MLlib features a speedy generalized linear algebra engine called Breeze. However, Breeze does not support certain popular features and functions in machine learning without the aid of third-party engines.

MLlib is interoperable with NumPy via Python, and can make use of diverse file systems and clusters including Hadoop, Kubernetes, and Apache Mesos.

MLlib's speed benefits and friendly high-level APIs are offset by limited scope and frequent dependence on slower systems such as Mahout over Hadoop. Unless you're committing to a Hadoop deployment (which brings related performance penalties), MLlib can take quite a bit of setting up.

Read more about Apache Spark in our Spark vs Hadoop review.

Sectors: Statistical analysis, business intelligence

Language: Java (with bindings for Python and R)

License: Apache License 2.0

H2O.ai is among the current leaders in the impetus to automate scalable machine learning by abstracting the development process into a GUI-driven 'dashboard' paradigm. H2O.ai features 'driverless AI', wherein a pipeline is developed algorithmically from a range of available ML approaches.

Models are automatically validated, tuned, selected and deployed, with a wide range available, from deep learning through to generalized linear approaches.

The web-based interface H2O Flow offers command line input but also a programming-free interactive environment that creates explorable graphical objects from a guided query process.

Scripts written in R or CoffeeScript are supported for model creation and analysis, as are Jupyter notebooks and cross-user collaboration.

Data imported to H2O.ai must be labelled, and pre-processing may be necessary to create suitably well-structured data. It may be easier to identify and exploit new insights from data reservoirs through less restrictive architectures, such as Hadoop. The limits of standalone H2O.ai are implied in its explicit support for Spark through Sparkling Water.

Sectors: Image, speech and handwriting recognition

Language: C++/Python

License: MIT

Microsoft Cognitive Toolkit (CNTK) is a deep learning framework that facilitates a range of neural network types, including convolutional neural networks, recurrent neural networks and feed-forward DNNs via directed graphs.

CNTK has full support for CUDA 10, and features automated differentiation and stochastic gradient descent with parallel implementation. CNTK performs well, scales easily and doesn't require intervention into low-level code. Besides C++, CNTK also supports Python and is accessible via a CLI.

CNTK's support for Keras ensures ongoing interoperability with the most popular competing frameworks, and it also comes its own model editor called BrainScript.

One of the first machine learning frameworks to support the Open Neural Network Exchange (ONNX) model interchange format, CNTK has support for MATLAB, Caffe 2, Keras, Chainer, PyTorch, and several other formats.

Cessation of further development after V2.7 was announced in 2019, as Microsoft moves to more abstracted self-automating systems via ML.NET. However, CNTK is arguably 'feature complete'.

If mobile or ARM support is required in your project, you will need to look elsewhere.

Sectors: NLP

Language: Python/Cython

License: MIT

SpaCy is a Python library particularly well-suited to text analysis. It's optimized for high-volume applications and operates at celebrated speed. It's written in Cython, a Python superset than can run in low-level and high-performing C-based frameworks.

Surprisingly, SpaCy has no in-built functionality for sentiment analysis. Instead, this functionality must be laid over SpaCy's provision for syntactic parsing and chunking, Stanford NER and word vectors.

SpaCy features a range of templated NLP models including classification, named entity recognition, and part-of-speech (POS) tagging. However, these are relatively inflexible, and may not suit all needs.

In terms of speed, rival frameworks have caught up somewhat with SpaCy over the last five years, albeit by sacrificing useful functionality, such as extended granularity when handling contractions.

Though it comes with higher hardware requirements than some competitors, and though it lacks architectural flexibility, SpaCy remains one of the most robust and high-performing Python libraries in the sector, as well as one of the most accessible and consistent.

Read more about SpaCy in our overview of Python sentiment analysis frameworks.

Sectors: Computer vision, NLP, speech recognition, Convolutional Neural Networks (CNNs)

Language: C++/Python/CUDA

License: Apache License 2.0

Thanks to its broad applicability, cross-platform support, close ties to GPU acceleration on the NVIDIA platform, and high-level industry backing, TensorFlow has become one of the most important libraries in machine learning over the last ten years.

Developed by Google, TensorFlow features support for GPU, CPU and Google's own ASIC Tensor processing unit (TPU). In addition to its use in computer vision applications, TensorFlow supports NLP, Time Series Forecasting, speech recognition, and text classification.

It's a user-friendly, powerful solution that both benefits from and contributes to NVIDIA's ascendancy in the machine learning space.

TensorFlow owes its versatility to a complex architecture that can be challenging to navigate, and which is completely committed to NVIDIA through the cuDNN libraries, despite fringe efforts to provide more diverse GPU support.

However, extensive high-level industry support tends to balance out TensorFlow's occasional performance shortfalls against lighter frameworks.

Read more about the framework in our Caffe vs TensorFlow comparison.