With digitization growing ever more important in nearly all fields of life, it’s no surprise that businesses around the world are increasingly investing in it at a frantic pace. Machine Learning (ML) and Artificial Intelligence (AI) in particular are witnessing significant adoption rates in the day to day operations of organizations.
The next few years are envisaged as tipping points for Machine Learning adoption. This is partly due to the several Machine Learning Frameworks that have been developed so far. Many of these are in vogue, making the question of which is the best machine learning framework a challenging one, as we shall see.
The selection of the right ML framework for your business is a daunting exercise, and getting to know the best fit for your product is certainly a challenge. With this article, we hope to offer a good grip of the most popular ML frameworks available today.
But first, let’s open with a brief rundown of Machine Learning and some of the top machine learning frameworks in 2024.
What is Machine Learning?
Machine Learning, ML for short, is an area of computational science that deals with the analysis and interpretation of patterns and structures in large volumes of data. Through it, we can infer insightful patterns from data sets to support business decision making - without or with very little need for human interface.
In Machine Learning, we feed large volumes of data to a computer algorithm that then trains on it, analyzing it to find patterns and generating data-driven decisions and recommendations. If there are any errors or outliers in information identified, the algorithm is structured to take this new information as an input to improve its future output for recommendations and decision making.
Simply put, ML is a field in AI that supports organizations to analyze data, learn, and adapt on an ongoing basis to help in decision making. It’s also worth noting that deep learning is a subset of machine learning.
What is a Machine Learning Framework?
A simplified definition would describe machine learning frameworks as tools or libraries that allow developers to more easily build ML models or Machine Learning applications, without having to get into the nuts and bolts of the base or core algorithms. It provides more of an end-to-end pipeline for machine learning development.
Which Framework is Best for Machine Learning?
There is no one “best ML framework”. The framework you choose will depend on the kind of application you are trying to build, as well as the data you are working with. Specifically, you’ll be factoring in such things as scalability, data processing, and deployment requirements, among other things. Once you read about the different ML frameworks, you’ll have a better answer to this question.
Is TensorFlow a Machine Learning Framework?
Yes, TensorFlow is an ML framework, one that was built by Google. It is very popular, and comes with several advantages, including extensive, flexible features, an exhaustive library for programming, classifications, regression models, neural networks, including a suite to write algorithms for software. However, it can take some to fully grasp.
Best Machine Learning Frameworks
With that understanding out of the way, let us dive into some of the most popular ML frameworks in detail.
1. TensorFlow
Key Features:
- Based on JavaScript
- Open source and has extensive APIs
- Can be used via script tags or via installation through npm
- Runs on CPUs and GPUs
- Extremely popular and lots of community supports
TensorFlow is an open-source, JavaScript library and one of the widely used Machine Learning frameworks. Being open-source, it comes for free and provides APIs for developers to build and train ML models.
A product of Google, TensorFlow is versatile and arguably one of the best machine learning frameworks. It provides extensive, flexible features, an exhaustive library for programming, classifications, regression models, neural networks, including a suite to write algorithms for software. It also runs on both CPUs and GPUs. One of the key advantages of TensorFlow is that it can be used in two ways: through Script Tags or installation via NPM.
One downside with TensorFlow is that it is not particularly beginner friendly. However, with good TensorFlow tutorials, you can accelerate your learning considerably.
Related Article: Pytorch vs Tensorflow
2. Shogun
Key Features:
- Written in C++
- Open-source and free
- Useful for designing algorithms and data structures, especially in the fields of education and research
- Connects with other ML libraries, including LibLinear and LibSVM, and several others
- Compatible with R, Python, Java, MatLab, C+ and other languages
Shogun is an open-source machine learning framework that works well with C++. It is free and useful for developers who want to design algorithms and data structures specifically for problems in the fields of education and research. Shogun can also connect with other ML libraries, including LibLinear, LibSVM, SVMLight, LibOCAS, among several others.
Additionally, Shogun is compatible with other languages and frameworks, such as R, Python, Java, Octave, C#, Ruby, MatLab, and Lua. Other highlights of this machine learning framework is that it helps implement Hidden Markov models, process large volumes of data, is user friendly, and provides several flexible features and functionalities.
3. Sci-Kit Learn
Key Features:
- Works well with Python
- The top framework for data mining and data analysis
- Open-source and free
Scikit-learn specifically supports development work in Python with an extensive library for Python programming language. Many users consider it to be one of the best ML frameworks for data mining and data analysis. Scikit-learn provides support for designing algorithms and models for classifications, clustering, pre-processing, regression, dimensional reduction, and model selection.
Also free and open-source, Scikit-learn is beginner friendly and comes with detailed documentation. Furthermore, it permits developers to change any pre-set parameters for algorithms when in use or during run-time. The framework is well suited for unsupervised and administrative calculations.
4. PyTorch
Key Features:
- Supports cloud-based software development
- Suitable for designing neural networks and Natural Language Processing
- Used by Meta and IBM
- Good for designing computational graphs
- Compatible with Numba and Cython
The PyTorch machine learning framework, based on Torch and Caffe2, comes with loads of options for optimizing algorithms. It is perfectly suited to design neural networks using the Autograd Module and Natural Language Processing.
PyTorch is open-source and supports cloud-based software development. It comes with multiple features, including libraries, tools, and distributed training. For User Interface development, it supports Lua. Pytorch is used by Meta and IBM. Good for designing computational graphs, its front end development is hybrid, which makes for versatile usability. The platform was designed to be integrated with Python and compatible with libraries such as Numba and Cython.
5. CNTK
Key Features:
- Written in C++
- Suitable for large-scale, multi-dimensional or sparse data sets
- Supports Python and BrainScript
Microsoft-owned CNTK is used to describe neural networks as a sequence of computational development steps in the form of directed graphs. It is an open-source ML framework designed with algorithms in C++ programming language and production readers. CNTK is an ideal choice for large-scale, multi-dimensional, or sparse data sets from C++, Python, and BrainScript. It allows developers to merge and view various ML model types, which includes recurrent networks, feed-forward deep neural networks, and convolutional neural networks.
6. Apache MXNet
Key Features:
- Adopted by Amazon for AWS
- Supports multiple languages, including Python, JavaScript, Julia, C++, Scala and Perl
- Microsoft, Intel and Baidu also support Apache MXNet
- Also used by the University of Washington and MIT
Apache MXNet was adopted by Amazon as its basic Machine Learning tool for AWS. It is distributed on a cloud infrastructure via a parameter server, and is scalable across several GPUs and servers.
MXNet supports multiple language APIs, which includes Python, JavaScript, Julia, C++, Scala, and Perl. Several IT companies support Apache MXNet, and that includes Microsoft, Intel, and Baidu. Leading research and educational institutions like the University of Washington and MIT also use Apache MXNet extensively.
7. H2O
Key Features:
- Developed for decision support system processes
- Prevalent in the risk and fraud propensity analytics, insurance customer analytics, and patient analytics in healthcare
- H2O-3 is compatible with Java, JSON, R, Python, Scala, and the Flow notebook/web interface
- Integrates with Hadoop and Spark
H2O is an open-source ML framework developed to cater to organizations in their decision support system processes. H2O is widely deployed for risk and fraud propensity analytics, insurance customer analytics, patient analytics in healthcare, advertising spend and ROI, and customer intelligence.
H2O has undergone three revisions, and the current version is H2O-3. This is an in-memory platform that is designed for distributed, scalable ML.
H2O-3 is compatible with interfaces such as Java, JSON, R, Python, Scala, and the Flow notebook/web interface. It also integrates seamlessly with big data technologies such as Hadoop and Spark. H2O-3 delivers implementations of many popular algorithms such as GBM, Random Forest, Deep Neural Networks, Word2Vec, and Stacked Ensembles.
H2O-3 is highly versatile and extensible, and developers can append data transformations and add customized algorithms of their preference, while having access to all client systems. To put it in simple terms - data collection is fairly easy, but decision making is tough. H2O-3 is for just that, and it makes it quick and easy to help derive insights from voluminous data through quicker and more accurate predictive models.
8. Apple’s Core ML
Key Features:
- Developed for macOS, iOS, and TVOS applications
- Beginner friendly
- Supports both CPUs and GPUs
Core ML was primarily developed for macOS, iOS, and TVOS applications and is highly beginner friendly, which includes graduates and self-taught developers.
Core ML is very comprehensive and provides a plethora of features such as image classification, sentence classification, natural language processing, barcode detection, a Gameplay Kit, and object tracking. With its unique low-level tech stack, it can provide excellent performance through the utilization of both CPUs and GPUs. Concerning security and ease of use, Core ML ensures user privacy and functionality of the app even in offline mode.
Machine Learning Intro for Python Developers
The Bottom Line
As we’ve seen, your preference and selection of a machine learning framework depends on the specific algorithms that the framework will run and other general requirements.
Today anyone with an internet connection can use machine learning frameworks and tools via cloud services provided by firms like Amazon, Google, and Microsoft. Furthemore, online machine learning courses have made it possible for anyone to build sophisticated ML applications and indeed many have done so.
With the increasing use of machine learning, organizations and businesses are now creating CoEs in ML to accelerate adoption and adaption of the technology. Very soon, ML will become so ubiquitous that it will touch almost every industry on this planet.
Want To Get Certified In Machine Learning?
Machine learning courses focus on creating systems to utilize and learn from large sets of data. Topics of study include predictive algorithms, natural language processing, and statistical pattern recognition.
Related Reading:
- What is Machine Learning?
- Difference between Data Science vs Machine Learning
- How to become a Machine Learning Engineer
- Machine Learning Algorithms
- Machine Learning Certification
- Machine learning Interview Questions
- Difference between Supervised vs Unsupervised Machine Learning
- Decision Tree in Machine Learning
- Machine Learning Algorithm
- Difference between Data Science vs Machine Learning
- Difference between Machine Learning and Deep Learning
