Machine Learning (ML) Frameworks

This article explains the top machine learning frameworks based on ML knowledge and results. You will require the ML framework tools whether you are a researcher, a startup, or a large organization looking to use machine learning. We also go through the basics and divisions of machine learning frameworks. Let's quickly learn about machine learning tools and their applications.

As the importance of digitalization grows in practically every aspect of life, it's not surprising that businesses are investing in it at an accelerating rate. In particular, organizations use machine learning (ML) and artificial intelligence (AI) more frequently in their day-to-day work.

Finding the machine learning framework that is best suited for your company may take time and effort. Determining which framework is the best option for your product can take time and effort. 

We hope that by the time you conclude this essay, you will have a strong understanding of some of the most extensively used machine learning frameworks currently accessible.

Machine Learning Frameworks - Table of Contents

What is Machine Learning?

Artificial intelligence (AI) includes a subset known as machine learning (ML), which enables machines to automatically learn from data and prior experiences while discovering patterns and making predictions with minimal human intervention.

Machine learning is a technique that can glean usable information from vast amounts of data by employing algorithms that can spot patterns and learn from experience in an iterative process. In machine learning, algorithms use computation methods to learn directly from data rather than relying on any predetermined equation that could serve as a model. It is a departure from the methods that have traditionally been used.

During the processes of learning, the performance of machine learning algorithms will adaptively improve with an increase in the total number of samples they have access to. It is because machine learning algorithms are designed to learn from their mistakes. For example, one of the subfields that fall within the broader machine learning category is "deep learning." It trains computers to reproduce natural human behaviors, such as learning from examples and other cognitive processes. It offers performance parameters that are superior to conventional machine learning algorithms.

With machine learning techniques, computers can function independently of human input. For ML applications to learn, grow, develop, and adapt independently, they are constantly being given new data.

It's an idea that lets machines learn new skills through observation and practice, all without needing hand-coding. We now have access to many best machine-learning frameworks that can facilitate this. Algorithms used in machine learning developed from traditional ones. They improve the intelligence of your programs by letting them automatically learn from the information you offer.

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What is a Machine Learning Framework?

A machine learning framework is a library, interface, or tool that enables developers to build ML models more easily and rapidly. It is accomplished without requiring the developers to delve into the specifics of the algorithms used behind the scenes. It offers a method that is straightforward and to the point for defining machine learning models by making use of a library of pre-built and optimized components. It makes the development process more efficient by preventing programmers from starting from scratch when creating a particular ML application. The creation of machine learning models is made more accessible by many similar libraries already in use inside machine learning frameworks.

In machine learning, one can achieve success by applying many cutting-edge tools. A machine learning framework is a collection of tools and algorithms that facilitate actions that are a part of the machine learning life cycle. 

The activities involved in the machine learning life cycle include

  • Data Engineering
  • Model development
  • Hyperparameter tuning
  • Testing
  • Logging
  • Monitoring 
  • Deployment.

[ Learn Complete Machine Learning Tutorial ]

What are the Challenges of a Machine Learning Framework?

The following are some of the primary obstacles that must be overcome by a machine learning framework to satisfy business requirements:

  • Technology Agnostic: To keep up with the ever-changing nature of the technology ecosystem, the machine learning framework must be flexible, modular, and scalable.
  • Skill Agnostic: Statistics, distributed systems, visualization, analytics, development, cloud computing, data engineering, and containerization are just a few examples of the diverse talents that need to be supported by the Machine Learning framework.
  • Machine learning Life Cycle: Even if the development of machine learning can be compared to the development of any other application, it must be significantly more dynamic to monitor data quality and model drift. It is necessary for there to be systems in place that allow for retraining and redeployment.

Which Framework is Best for Machine Learning?

There is no such thing as a "best ML framework." The framework you select will be determined by the type of application you are attempting to create and the data you are working with. Scalability, data processing, and deployment needs, among other things, will be taken into account. You'll better answer this question once you've read about the various ML frameworks.

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Is TensorFlow a Machine Learning Framework?

Yes, TensorFlow from Google is a machine learning framework. The company was responsible for its creation. It has a large user base and comes with a variety of helpful features and benefits, such as extensive and adaptable functionality, a comprehensive library for regression models, classifications, programming, and neural networks, as well as a set of ML tools that can be used to compose algorithms for software. In addition, it has a large user base. Some individuals may need some time to comprehend it properly.

[ Check out Top Machine Learning Projects ]

15 Top Machine Learning Frameworks

Machine learning frameworks focus on mathematical and statistical modeling (machine learning), not training neural networks (deep learning). Still, the work you want will determine which framework you should use.  Here are some of the most popular machine learning frameworks that are helpful to manage machine learning projects will be discussed below in detail. Let go now!

  • TensorFlow
  • Keras 
  • MXNet
  • Caffe
  • H2O
  • Theano
  • Shogun
  • WEKA
  • Spark MLLib
  • Scikit Learn
  • ML Pack
  • Apache Singa
  • PyTorch
  • Amazon Machine Learning
  • Azure ML Studio.

An overview of some of the most widely used ML frameworks is as follows:

1. TensorFlow

When it comes to deep learning and machine learning, TensorFlow is a prominent framework used by both programmers and academics. The Google Brain team introduced TensorFlow in 2007, and since then, it has developed into a full-fledged machine learning ecosystem. It also aids in data pre-processing, feature engineering, and model serving, expanding its use beyond simple training.

TensorFlow's main library is used on ARM and AMD systems after being imported as a Python module. Train and deploy models in the browser and on Node.js with the help of the TensorFlow.js JavaScript library. The TensorFlow version is a simplified variation of the original TensorFlow software for model inference on mobile, IoT, and edge devices.

[ Learn Complete TensorFlow Tutorial ]

2. Keras 

Keras is another library that is highly productive and focuses its attention solely on the process of resolving issues that are associated with deep learning. Considering that we have been discussing deep learning and its complexities, it is appropriate to mention Keras. In addition, Keras assists engineers in making the most of the scalability and cross-platform capabilities they can use for their applications. The Open-ended Neuro-Electronic Intelligent Robot Operating System (ONEIROS) project is where it was initially shown to the public in the year 2015.

Keras is a popular Python interface for machine learning and deep neural networks. Today, major IT companies like Uber, Netflix, Etc, use Keras to increase their scalability.

3. Apache MXNet

Apache MXNet is one of the more recent additions to the ecosystem of deep learning, having first been released in 2017. Its compatibility with a wide range of programming languages makes it stand out from other similar applications. These include Python, C++, Julia, Java, JavaScript, Matlab, R, Go, Perl, Scala, and Wolfram.

Apache MXNet was created through a collaborative effort between academics at Carnegie Mellon University and the University of Washington, led by Carlos Guestrin. Amazon has chosen it as the primary deep learning framework for the company's AWS-managed machine learning platform solutions and the development of commercial goods. Apache MXNet is used to implement most of the pre-built models and algorithms currently accessible through Amazon SageMaker.

MXNet offers Python developers a robust and versatile application programming interface (API). This API is designed for developers with varying degrees of experience and a wide variety of needs. Apache MXNet exposes Gluon API, which provides a clean and easy API for deep learning. It is comparable to how Keras offers TensorFlow's user-friendly and high-level application programming interface (API). Gluon is equipped with various application programming interfaces (APIs) for computer vision, natural language processing, and time-series analysis.

4. Caffe

Caffe was developed as a flexible, fast, and expressive framework for deep learning. The Berkeley Vision & Learning Center (BVLC) and members of the public work together to create it. During his doctoral studies at Berkeley, Yangqing Jia developed the idea.

Caffe is made available to the public via the BSD 2-Clause license. Users can toggle between the graphics processing unit (GPU) and the central processing unit (CPU) and model and optimize with minimal coding effort. Caffe's rapid processing time makes it ideal for scientific study and widespread commercial use. Caffe uses a single NVIDIA K40 GPU to process approximately 60M photos daily.

5. H2O

One more free and open-source ML library is H2O. It's geared toward corporate use, and it employs predictive analytics and mathematics to facilitate data- and insight-driven decision-making. This artificial intelligence application combines cutting-edge, open-source Breed technology with other advantages, including a familiar and straightforward Web-based user interface (GUI) and compatibility with all standard database and file formats.

Python, JavaScript, Java, R, Scala, JSON, and a web interface are just some of the models included with H2O. All H2O's basic functionality is written in Java, and the REST API makes it possible for scripts or any external program to access it. Existing AI tools and programming languages can work in Hadoop systems without modification. H2O has applications in areas as diverse as insurance analytics, healthcare, advertising technology, fraud analysis, and consumer intelligence.

6. Theano

Theano is an ML Python programming language library that allows users to write, optimize, and evaluate mathematical expressions, particularly those using multi-dimensional arrays (NumPy.ndarray).

When dealing with issues that include a significant quantity of data, it is possible to use Theano to achieve speeds comparable to those of hand-crafted C implementations. It was developed at the LISA lab to facilitate the quick creation of practical machine-learning algorithms. The Greek mathematician Theano, who some belief was Pythagoras' wife, is the inspiration for the ship's name. Theano is distributed with a license based on BSD standards.

[ Check out Top 10 Machine Learning Algorithms ]

7. Shogun

Shogun is one of the most venerable and oldest machine-learning libraries. Shogun was developed in 1999 and written in C++. However, its functionality is not restricted to functioning only in C++.

Shogun can be used transparently in various languages and environments, including Python, Java, Ruby, C#, R, Octave, Lua, and Matlab. It is made possible through the SWIG library. Shogun is intended for unified, large-scale learning across a diverse range of feature types and learning scenarios, such as exploratory data analysis, regression, or classification.


When working with WEKA, users can access many machine-learning techniques. Experts in machine learning can extract useful information from massive amounts of data using the ML framework methodologies. Within this setting, the professionals can establish an environment to develop new machine-learning techniques and put those strategies into practice using actual data.

Researchers in the disciplines of machine learning and applied sciences make use of WEKA to achieve their goals in terms of learning. It is a valuable instrument that may be used for many data mining tasks and responsibilities.

9. Spark MLLib

Spark MLlib is a plugin atop Spark Core and offers application programming interfaces (APIs) for machine learning primitives. For model training, machine learning often deals with large amounts of data.

The fundamental computing framework that Spark provides is an incredible asset. In addition to this, MLlib is a source for the majority of the most widely used machine learning and statistical methods. It dramatically simplifies the process of working on a massive machine-learning project.

10. Scikit Learn

SciKit Learn is free and easy to use, even for people who need to learn more about machine learning. It also comes with much documentation. It simplifies the process of tuning and debugging models by allowing the developer to alter the algorithm's predefined parameters while the method is being used or during runtime.

With its rich Python library, SciKit-Learn makes it possible to construct machine-learning applications. In terms of data mining and analysis, it is one of the most effective technologies currently on the market. The pre-processing capabilities of Sci-Kit Learn are vast, and the software also makes it possible to construct algorithms and models for clustering, classification, regression, dimensionality reduction, and model selection.

Learn Top Machine Learning Interview Questions and Answers that help you grab high-paying jobs

11. ML Pack

mlpack is a machine learning library written in C++ that was released for the first time in 2011. According to the library's developers, the package was developed with "scalability, speed, and ease of use" in mind. Depending on the complexity of the work at hand, developers can choose between using a C++ API or a cache of command-line executables to implement mlpack.

mlpack presents these algorithms as simple command-line scripts and C++ classes which may later be integrated into larger-scale machine learning solutions.

12. Apache Singa

Apache Singa is an extensive distributed deep learning framework that uses large datasets to train big deep learning models. It was designed using a simple programming model based on the layer abstraction.

A wide range of well-known deep learning models is supported, including feed-forward models like energy models like restricted Boltzmann machines (RBM), convolutional neural networks (CNN), and recurrent neural networks (RNN). Users can choose from a variety of built-in layers.

13. PyTorch

Facebook is responsible for developing the open-source Deep Learning framework known as PyTorch. It used the Torch library as its foundation and was created with one fundamental goal: to speed up the entire process, from research prototyping to production deployment. One of its most noteworthy features is that PyTorch includes a C++ frontend in addition to a Python interface.

The "torch. distributed" backend encourages scalable distributed training and performance optimization in research and production, and the front end acts as the primary foundation for model creation. You can't get much better than this regarding deep learning frameworks.

14. Amazon Machine Learning

Amazon Machine Learning, also known as Amazon ML, is a powerful service hosted in the cloud that makes it simple for software developers of varying levels of experience to implement machine learning technology.

Amazon ML gives users access to visualization tools and wizards that walk them through the process of developing machine learning (ML) models without requiring them to grasp the complicated ML methods and technology involved in the process. Once your models are complete, Amazon ML makes it easy to receive predictions for your application using simple APIs. You won't have to create custom prediction-generating code or manage any infrastructure because Amazon ML does everything for you.

15. Azure ML Studio

Microsoft Azure customers can use Azure ML Studio to build and train models, then publish them as APIs for usage by others applications. The service provides up to 10 GB of storage per user for model data, while users can link their Azure storage to the service for larger models.

Algorithms from both Microsoft and outside developers are readily available. You can test out Azure ML Studio for up to eight hours without providing any personal information before deciding whether or not to sign up for an account.

[ Learn More About Azure Machine Learning ]

Machine Learning Framework FAQs

1) What is a Machine Learning Framework?

The ML framework is a collection of tools that let developers construct and deploy machine learning models more quickly and easily. Businesses can use this technology to securely scale up their machine learning efforts while keeping their ML lifecycle in excellent shape.

2) Is PyTorch better than TensorFlow?

TensorFlow provides improved visibility, allowing developers to troubleshoot and track the training process more effectively. However, PyTorch only offers limited visualization. TensorFlow also outperforms PyTorch when deploying learned models to production, owing to the TensorFlow Serving framework.

3) Which ML Framework is best?

TensorFlow is one of the most well-known machine learning frameworks, and it comes with a library of already pre-trained models. This framework assists engineers, and deep neural scientists develop deep learning algorithms and models.

4) Which Framework is best for AI?

Theano comes packaged with a library of algorithms that produce neural network operations when applied to data frames. It is now the most common AI framework used by developers using Tensorflow or Theano, and it works with C++, Python, Julia, Java, and Scala. Additionally, it works with Tensorflow.

5) Is TensorFlow a Framework?

Yes, TensorFlow is a framework that may be used to perform machine learning, deep learning, and other statistical and predictive analytics tasks. This framework was developed by Google researchers and is available as open source.


The machine learning frameworks and tools mentioned in this article speed up and make the process of developing algorithms more accessible. In addition to the ML frameworks available within the open-source community, many of today's most prominent businesses have also developed their ML frameworks to employ inside their operations.

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Last updated: 04 Apr 2023
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Viswanath is a passionate content writer of Mindmajix. He has expertise in Trending Domains like Data Science, Artificial Intelligence, Machine Learning, Blockchain, etc. His articles help the learners to get insights about the Domain. You can reach him on Linkedin

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