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Top 10 Open-Source Frameworks for AI and Machine Learning Models

written by Harshita Khangarot | Jan 22, 2020
Open-Source Frameworks for AI and ML Models

Artificial intelligence and machine learning are both fields of computer. Both are working to make smarter machines that can recreate human behavior. The adoption of machine learning and artificial intelligence has increased remarkably. Now, AI and ML are being used in every field, be it spacecraft designing or healthcare, banking, and finance department.

The global artificial intelligence software market is expected to experience massive growth in the coming years, with revenues increasing from around 9.5 billion U.S. dollars in 2018 to an expected 118.6 billion by 2025. (source)

Top Open-Source Frameworks for AI and ML Models

Open-Source Frameworks for AI and ML Models

With the advancement of AI and ML, there are a variety of open-source frameworks available in the market. Here we will discuss some of the top open-source frameworks.

1. Tensorflow

Today Tensorflow has become one of the most popular tools for machine learning and artificial intelligence solution providers. There is a library in Tensorflow named as “JS Library” that helps in Machine learning development. Tensorflow is available in many languages like C++, Python, Java, Go, Rust, Haskell, and JavaScript.

Features of Tensorflow

  • End to end a deep learning system
  • Highly flexible
  • Perform numerical computations
  • Build and train ML models effortlessly

    2. Scikit-Learn

Scikit-Learn is written in Python Language. It has some emerging ML models like clustering, regression, classification, and dimensionality reduction. Scikit-Learn focuses on data analysis and data mining. Three other open-source projects were used to design Scikit-Learn those were NumPy, SciPy, and Matplotlib.

Features of Scikit-Learn

  • Reusable tool in many contexts
  • Commercially useable underneath BSD license
  • Efficient for data analysis and data mining

machine learning framework3. Google Cloud ML Engine

Google Cloud ML engine is not a SaaS program. For the accuracy of the predictions, the Google cloud ML engine can perform hyperparameter tuning apart from hosting and training. It is a hosted platform to run ML predictions and training jobs at scale. A model that is trained in the external environment, to deploy that model this service can be used.

Features of ZGoogle Cloud ML Engine

  • Used to train a complicated model.
  • Provides ML model building, training, predictive modeling, and deep learning.
  • SErvices like prediction and training can be used separately and jointly.

    4. Amazon Machine Learning

Amazon machine learning (AML) are mostly used to upgrade or enhance the skills for any developer or web developer. AML integrates data from various sources like RDS, Redshift, and Amazon S3. AML makes machine learning more accessible for developers by using easy to use analytics and visual aids.

Amazon Machine Learning Framework

Features of AML

  • It supports three types of models, which are Regression, multi-class classification, and binary classification.
  • AML provides visualization and wizards tools.
  • Batch predictions, ML models, Real-time predictions, Evaluations, and Data sources are fundamental concepts of AML.

    5. Shogun

Shogun was developed in C++ language but can be used with other languages, like Ruby, Python, Java, Matlab, R, C#, Lua, and Octave. Shogun can process an immense amount of datasets like consisting of up to 10 million samples. It offers data structures and algorithms for ML problems.

Features of Shogun

  • A prominent tool for beginners.
  • Specially Designed for extensive scale learning programs
  • Shogun tools allow linking to different ML libraries like SVMLight, LibOCAS, and other LibLinear tools, etc.

    6. Apache Spark MLlib

Apache spark MLlib is an ascendable ML library that runs on Hadoop, standalone, Apache Mesos, Kubernetes, or in the cloud. It is able to access the data from multiple resources and can process large scale data. This machine learning library can be used in Python, R, Java, and Scala.

Features of Apache Spark MLlib

  • Easy to use
  • It contains high-quality algorithms.
  • It integrates many utilities and algorithms like regression, classification, and clustering.

    7. Accord.NET

Accord.NET is written in C#. It is used for artificial neural networks visualization, and numerical optimization. It is an excellent ML tool for creative as well as general tasks. Tasks like image joining, face recognition, and tracking moving objects are operated by its visual image processing algorithm.

Features of Accord.NET

  • Supports Windows
  • Makes image and audio processing easy.
  • Designed for constructing production-grade signal processing, computer audition, statistics applications, and computer vision.

    8. Caffe

Caffe is an abbreviation of Convolutional Architecture for Fast Feature Embedding. Caffe is written in C++. It is also a deep learning tool that is used to create apps. Caffe comes with C++, Matlab, and Python interface. Caffe supports numerous Operating Systems (OS) like Windows, Mac OS X, and Ubuntu.

Features of Caffe

  • Expressive architecture
  • Fast performance
  • Vibrant community

    9. Theano

Theano is one of the oldest libraries. With the help of Theano, one can easily function various ML models. Theano was designed for deep learning, and it is a Python library. Theano is usually supported on all kinds of operating systems like Windows, Linux, and Mac OS X.

Features of Theano

  • Capable of transforming structures into efficient codes
  • Provides efficient symbolic differentiation
  • Comes with extensive code-testing capabilities

    10. Keras

Keras is written in Python programming language. Keras can be considered as a high-level neural network application programming interface (API). It can run on frameworks like Microsoft Cognitive Toolkit (CNTK), Tensorflow, etc. It is a framework that is user-friendly and provides ease of extensibility and modularity.

Keras open source frame work for maching learing

Features of Keras

  • Ideal for fast prototyping
  • Optimized for both GPU and CPU
  • Simplify the creation of deep learning models

Conclusion

Selecting one machine learning framework out of all is difficult to choose. But before making a final decision, it is better to decide the perfect ML model for building an application. Apart from this, select a framework that fulfills all the requirements that are required. Hire a Machine Learning Development Company that can provide you with the best possible solutions at affordable prices and even help you stand out against your competitors.

Harshita Khangarot

A skilled content writer and a lifelong learner with an ongoing curiosity to learn more, I specialize in technical, banking, hospitality, and poetry content writing; while taking every opportunity as a responsibility and believe in creativity that is beneficial for society.

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