10 Best Machine Learning Frameworks in 2020

10 Best Machine Learning Frameworks in 2020

MI is a widely adopted technology with an increasing pace of digitalization progress. Still, MI is essential for businesses, and they use machine learning and artificial intelligence in their everyday operations.

As technology advances in 2022, various machine learning frameworks will come to light. Knowing which software fits your needs could mean the difference between a successful business investment and a failed venture.

Here, we’ll discuss what machine learning is, and the best-Machine-Learning-frameworks in 2022 are:

What is machine learning?

Machine learning is a growing obsession that concerns data analysis and data science. The ever-increasing demand for AI and ML means mobile app developers need to step up their game and learn the difference in types of machine learning.

Machine Learning is a sub-domain of artificial intelligence that helps businesses examine the data, learn, adapt, and make decisions accordingly. In addition, it assists in system processing, automates repetitive admin tasks, and more.

Furthermore, Deep Learning is a subsidiary of machine learning. It offers efficiency and accuracy that exceed the profound cognitive abilities of the human brain. The demand for higher proficiency urges data scientists to develop intuitive and innovative Deep Learning platforms that boost a company's productivity.

The best Deep Learning frameworks are either an interface or tools that help developers construct Deep Learning models easily. Additionally, it eliminates the need to understand the details of the ML/DL algorithms of the Machine Learning and Deep Learning platforms.

Machine Learning can be divided further into three types:


Supervised machine learning comprises predicting and sorting through data based on past inputs—for example, email software like Gmail and Yahoo filter spam emails via machine learning algorithms.


Facebook is an excellent example of unsupervised machine learning. The ML frameworks of the social platform detect the hidden patterns and recognize facial features in pictures and your friend list to suggest recommendations.


Reinforcement Machine learning is primarily concerned with boosting efficiency.

15 machine learning frameworks in 2022

Machine Learning Frameworks are tools for developers to understand and design machine learning models. It helps create powerful AI software without diving into the technicalities of the algorithms.

Machine Learning helps speed up data processing and increase the accuracy of results. Here are some of the best-Machine-Learning-frameworks in 2022 that developers should consider when integrating AI into mobile apps or other software:

1. TensorFlow

TensorFlow is the best open-source machine learning library of JavaScript. It is a free platform with APIs that help build and train the ML models.

Developed by Google, TensorFlow is the best framework for machine learning that offers extensive, flexible features, a vast programming library, and resources for an array of development tasks. It also supports classifications, regressions, and neural networks, including writing algorithms for software.

Developers can run the framework on CPUs and GPUs. One of the advantages of TensorFlow is that it can be used in two ways, Script Tags or by installation via NPM. Additionally, it can assist in human pose estimation.

The drawback, however, is that it is challenging to learn and understand for beginner developers.

2. Google cloud ml engine

Cloud ML Engine is one of the best Deep Learning frameworks that offer training for amateurs, among other services for building machine learning models. In addition, it helps developers and data scientists with forecasting in various fields and domains.

The Machine Learning framework is the optimum choice for ML model training and building, plus deep learning and predictive modeling. Furthermore, training and predictive services can be used independently or together. For example, enterprises often use the framework to speed up customer email responses and detect clouds in a satellite image.

3. Apache mahout

Apache Mahout is one Deep Learning platform operating on a distributed linear algebra framework to scribe and implement ML algorithms. The scalable algorithms are for classification, clustering, and batch-based collaborative filtering.

Developed by Apache Software Foundation, it is also an open-source, free platform that uses the MapReduce paradigm and runs on top of Apache Hadoop. Furthermore, as one of the best web frameworks for machine learning, Mahout offers matrix and vector libraries, distributed fitness functions for programming, and more.

Benefits of Apache Mahout include the fact it works for extensive data sets, is simple to operate, and is extensible. On the other hand, it requires detailed documentation for ease of use, plus some algorithms are missing.

4. Shogun

Shogun is another open-source machine learning framework compatible with the C++ programming language. It is a free platform developer can use to design algorithms and data structures, primarily for ML problems in education and research.

Gunnar Raetsch and Soeren Sonnenburg designed Shogun in 1999 to support vector machines for classifications and regression problems, plus large-scale learning. In addition, it allows developers to connect with other machine learning libraries, including LibLinear, LibSVM, SVMLight, LibOCAS, etc.

Furthermore, Shogun also supports other languages and frameworks, including R, Python, Java, Octave, C#, Ruby, MatLab, Lua, etc. Among the perks of the ML framework is that it helps implement Hidden Markov models, can process extensive data sets, is convenient, and is filled with flexible features and functionalities.

5. Sci-kit learn

Sci-Kit Learn is the best web framework for machine learning that supports development in Python with a library for Python programming language.

It is one of the best frameworks for machine learning used for data mining and data analysis. Sci-Kit Learn also supports designing models and algorithms for classifications, regression, clustering, pre-processing, Dimensional reduction, and Model selection.

Sci-Kit Learn is a free platform that is easy to understand for even amateur developers, with its detailed documentation readily available. Additionally, it allows changing the pre-set parameters for algorithms when in use. Therefore, the framework is preferred for unsupervised and administrative calculations.

6. Pytorch or Torch

PyTorch or Python ML framework, based on Torch and Caffe2, supports Lua programming language for the user interface. The platform is an ML library used by Facebook, IBM, Idiap Research Institute, and Yandex.

Torch is used to design neural networks using Autograd Module and natural language processing with tons of options for optimizing algorithms. It is an open-source, Deep Learning platform that supports cloud software development and provides multiple features, including tools, libraries, and distributed training.

The Torch is the best choice for designing computational graphs, and it is a hybrid front-end that simplifies its usability. The platform was designed to integrate Python and used with libraries like Numba and Cython.

7. H2o

Another open-source Machine Learning framework on the list is H2O. It is an AI tool designed to assist businesses in decision-making based on the available data. The platform is primarily used for risk and fraud analysis, insurance analytics, predictive modeling, healthcare, advertising, and customer intelligence.

8. Microsoft Cognitive Toolkit (CNTK)

A Machine Learning platform from Microsoft, CNTK, describes neural networks as a sequence of computational steps through directed graphs. It is also an open-source framework designed with algorithms in C++ programming language and production readers.

CNTK is best when dealing with large-scale, multi-dimensional, or sparse data sets from C++, Python, and Brain Script. It helps developers merge various ML model types, including recurrent networks, feed-forward deep neural networks, and convolutional neural networks.

9. Apache MXNet

Amazon adopted the Apache MXNet Deep Learning framework as its primary Machine Learning tool on AWS. The platform was designed to be distributed on a cloud infrastructure via a parameter server. As a result, it can scale across multiple GPUs and servers.

Furthermore, MXNet is compatible with various programming styles and supports multiple language APIs, including Python, JavaScript, Julia, C++, Scala, Perl, and Go. In addition, multiple organizations support the ML framework, including Microsoft, Intel, and Baidu, plus many research institutions like the University of Washington and MIT.

10. Apple’s core ML

Core ML focuses on developing machine learning models compatible with MacOS, iOS, and TVOS applications. As a result, it is convenient to use for even beginner developers.

The ML platform allows image classification, sentence classification, natural language processing, barcode detection, Gameplay Kit, and object tracking.

The platform’s significant advantage is leveraging CPUs and GPUs for phenomenal performance with its low-level tech stack. Furthermore, it helps ensure the privacy and functionality of the app, even when offline.

11. Keras

If you want a straightforward framework to understand, Keras is the ideal option. However, one of its most significant advantages is this. If you’re going to be proficient with a new framework, you must first learn it.

Keras has another advantage: it is lightweight. It is essential and minimalistic. Any framework, regardless of type, performs best when it is light. What makes this so? As a result, you will have a better performance. After all, fewer resources are required to complete the task. Keras, too, is a quick runner.

12. Caffe

This framework was introduced in 2017. However, it was created in C++, and it is a suitable choice for many software engineers and programmers, as C++ is still a popular language.

Another benefit of Caffe is that it works flawlessly with a good GPU. For example, it can easily handle the processing of tens of millions (literally) of photos. And it's all done in a single day. So, if speed is a must-have, Caffe is still a great framework.

13. Theano

Although this tool isn't the most recent, it is still one of today’s most widely used AI frameworks. Python is the programming language on which it is built.

One of its most significant advantages is this. Python is a widely-used programming language, and machine learning and artificial intelligence are two of its most vital features. So, assuming you're already familiar with Python (which is easy to learn), getting acquainted with Theano shouldn't be too difficult.

Even though it is an older library, it is still widely used in business. Many more tools are comparable to this one in some manner. Perhaps in their design or functionality.

14. Auto ML

The best-automated machine learning framework is the most powerful and latest addition to a machine learning engineer's toolkit. Optimizations are crucial in machine learning tasks, as you may know. As a result, humans arrive in a modern world of Meta, where software aids in creating software. Therefore, many developers utilize this best-automated machine learning framework to improve their models, a machine learning library.

15. Hugging face.co

Hugging face.Co's machine learning library builds strong basic models for researchers on TensorFlow and PyTorch. They are complicated tools, such as GPT-2, which is simplified for use on your PC. It's one of the most powerful machine learning frameworks available in 2022.

FAQS about AI frameworks

What is the best framework for AI?

Machine learning is a subset of artificial intelligence, which has been around for a long time and is now ubiquitous. Machine learning frameworks and technologies have made it user-friendly for the IT industry. Machine learning was difficult to foresee a few decades ago. Still, it is now employed in various industries, including healthcare, where it is used in medical imaging, natural language processing of medical records, and genetic information. Machine learning (ML) is rapidly altering practically every aspect of human existence. People appear to be becoming addicted to technology in communicating and traveling.

What are frameworks in machine learning?

TensorFlow, PyTorch, and sci-kit-learn are perhaps the most popular machine learning frameworks.

Still, the framework you employ will be determined by the task you're trying to do. In contrast to neural network training, these frameworks are focused on mathematics and statistical modeling (machine learning) (deep understanding).

What is the most popular deep learning framework?

Here’s a quick breakdown of these popular ML frameworks

  • Because of their similarities, TensorFlow and PyTorch are direct rivals. Both offer a comprehensive set of linear algebra tools and the ability to do regression analysis.
  • Scikit-learn has been around for a while and is most recognizable among R programmers, but it has one major drawback: it is not designed to run across a cluster.
  • Apache Spark is all about clustering, and Spark ML is engineered to run on a cluster.

Which framework is best for NLP?

Many organizations rely on processing enormous volumes of natural language data in various media (pictures or videos) on the web to generate more value-added services for their consumers. Therefore they use natural language processing (NLP). NLP tools simplify handling tasks such as document classification, topic modeling, part-of-speech (POS) tagging, word vectors, and sentiment analysis. NLP has been part of our lives for decades. We interact with NLP daily without even realizing it.

The following Frameworks are used for NLP:

AllenNLP, Apache OpenNLP, Apache Tika, BERT, Bling Fire, ERNIE, FastText, FLAIR, Gensim, Microsoft Icecaps, Jiant, etc.

Which framework is the best for you?

I hope the article provides an excellent Deep Learning framework comparison to help you understand the different types available for designing ML models.

Your choice of machine learning frameworks depends entirely on the algorithm requirements, your expertise, and the client’s budget. Our list, however, contains ML tools that are free to use. In addition, our mobile app experts at Cubix are ready to offer more insights and support for Machine Learning frameworks. So, share your idea and explore the applications of ML for your company.

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