Top 20 machine learning frameworks of all time
By: Hitesh Gupta
Tags: Mobile App Developers, Mobile App Development, Mobile Apps, Website Development

Machine learning and artificial intelligence solutions are enhancing business operations in Indian enterprises across industries. In fact, the Indian AI market is valued at $6.4 Billion, with MNC IT, Technology, and Electronics companies (36%), domestic IT companies (25%), and captive firms (8%) holding strong market share in India. Close to 91000 AI experts, across enterprises and start-ups, are continually working to expand the scope of the technology every day.

While the right machine learning framework for your project, depends on its scope, flexibility, scalability, and technological capabilities, the following frameworks can help you assess the ML ecosystem better. Let us review the top 20 machine learning frameworks of all time, to understand their role in enhancing ML adoption, increasing output productivity, and driving greater performance across projects.

1. TensorFlow

TensorFlow, an open-source Google Brain project, is one of the most popular ML frameworks available in the marketplace. It is a complete machine learning research and production tool and is simple to use for managing dynamic data sets. TensorFlow users need a complete understanding of NumPy, a Python framework, for working with n-dimensional arrays seamlessly. Many top web development services providers use TensorFlow for scale-driven data processing.

2. Scikit-Learn

For faster processing and analytics initiatives, developers can use scikit-Learn frameworks to run quick machine learning models. It is a Python-based package and can execute key tasks such as regressions, SVMs, descent models, etc. It is a popular starter ML framework, after which developers can select larger-scale frameworks for completion.

3. PyTorch

PyTorch, developed by Facebook AI Research (FAIR), can perform the regression, classification, and neural network tasks effortlessly. It also runs on CPUs and GPUs and is widely considered to be highly customizable. For larger projects, PyTorch can drive faster training times and can be used for custom web application development seamlessly.

4. Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit is generally a deep learning framework that is ideal for executing convolutional neural networks, recurrent neural networks, and feed-forward DNNs based tasks. It is a highly customizable framework and can use multiple GPUs providing seamless parallelization.

5. Caffe

Convolutional Architecture for Fast Feature Embedding (Caffe) is a highly effective deep learning framework in C++, with a robust expression architecture enabling ease of transition from CPU to GPU. It is the ideal framework for executing image classification and segmentation processes at a much faster rate.

6. Theano

Theano is an open-source ML framework, that handles mathematical expressions effectively, including multidimensional arrays. It provides dynamic C code generation and combines a computer algebra system (CAS) with an optimizing compiler for faster processing.

7. Keras

Keras is the right framework to bridge the gap between machine learning and deep learning, through a simplified interface and streamlined approach to data processing. It is widely used for classification and summarization, and developers can also use it for speech recognition, tagging, and translation.

8. Spark ML

SparkML allows developers to run in clusters, which can enable them to execute complex matrix multiplication exercises. The framework requires a distributed architecture so that it can work with larger data sets. Developers with experience in Spark can leverage the framework’s Spark RDD data structures for seamless processing.

9. Torch

Torch is one of the earliest ML frameworks in the marketplace, launched in 2002. Torch is simpler to use, and its LuaRockspackage manager ensures seamless data processing at-scale. The framework has easy to use features to enable effective slicing and analysis of data tables. Developers familiar with TensorFlow can leverage the capabilities of Torch as well.

10. Accord.NET

Accord.NET is a C# based ML framework that is widely used for executing statistics, ML, and neural network tasks. While developers can perform classification, regression, and clustering seamlessly, they can also benefit from the extensive audio and image processing libraries provided.

11. Firebase ML Kit

Firebase provides multi-platform capabilities of handling text, image and object recognition, and classification. Web development services providers use Firebase to help integrate machine learning models into mobile applications without technical load.

12. Scikit-Garden

Scikit-Garden is specifically designed for decision trees and forests, thereby localizing its applications to the domain. It can handle complex tree structures seamlessly and can be easily used by developers and enterprises.

13. Apache Mahout

Apache Mahout is a free to use ML framework that is extensively leveraged for linear algebraic tasks, for data scientists and researchers in the domain. Apache Spark is used for most of the clustering, classification, and filtering while distributing alongside an interactive shell and library.

14. Chainer

Chainer is an easy to use ML framework leveraged for custom web application development for back-end processing of real-time data. Sentiment analysis, machine translation, and speech recognition can be seamlessly performed using this highly flexible framework.

15. Shogun

Shogun is a popular C++ based ML framework that is a highly advanced interface used extensively for research-driven purposes. It is the ideal open-source solution for large-scale data processing and supports multiple languages such as python, Octave, R, Java/Scala, Lua, C#, Ruby, etc.

16. Weka

Weka is a Java-based ML framework that has pre-existing models for deep data mining, data learning, and pre-set processes. It provides extensive access to other platforms for distributed tasks while being used as a research tool for many projects.

17. Dask

The open-source python ML framework provides extensive support to other machine learning frameworks to simplify task execution. It extends parallel computing and enables additional improvements for large-scale enterprise projects.

18. ONNX

The Open Neural Network Exchange enables seamless ML processing, through CPUs and GPUs, and is a highly effective platform to transform ML work from one framework to another. When custom web application development requires interoperability between ML architectures, ONNX is leveraged.

19. Huggingface

Huggingface is a new ML framework that provides workable base models for many ML tasks. Researchers can leverage the framework to perform complex tasks easily, especially in the neural networks and NLP domains.

20. Amazon SageMaker

The fully integrated IDE for machine learning is extensively used for computer vision, image analysis, forecasting, and text analytics across a wide range of applications. Developers can use SageMaker to build and deploy several ML models on the cloud.

In conclusion

There are several benefits of opting for the right machine learning framework that fits perfectly with your organizational needs. By understanding the role and impact of each framework, a more informed decision can be executed to select the right alternative. Developers, start-ups, and organizations need to consider key factors such as scope, scale, community, performance, and versatility in applications.