Top 10 Open-Source Frameworks for AI and Machine Learning Models
written by Harshita Khangarot | Jan 22, 2020
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)
Table of Contents
Top 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.
Features of Tensorflow
End to end a deep learning system
Perform numerical computations
Build and train ML models effortlessly
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
3. 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.
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.
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.
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
Makes image and audio processing easy.
Designed for constructing production-grade signal processing, computer audition, statistics applications, and computer vision.
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
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
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.
Features of Keras
Ideal for fast prototyping
Optimized for both GPU and CPU
Simplify the creation of deep learning models
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.
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