Ever since Machine Learning became a mainstream technology tool in the industry, the popularity and demand of Machine Learning frameworks have skyrocketed. In fact, ML frameworks have become a standard paradigm in the development of AI/ML models and applications, and rightly so. The greatest benefit of ML framework is that they democratize the development of ML algorithms and models while simultaneously expediting the whole process.
In simple words, a Machine Learning framework is a tool, library, or an interface that allows ML Developers/Engineers to build efficient ML models quickly, without needing to dig deep into the details of the underlying algorithms.
They offer a concise and straightforward approach to defining models by employing a host of pre-built and optimized components. Thanks to their ease-of-use factor, ML frameworks are steadily gaining ground beyond the open-source community to being leveraged by large corporations as well.
Table of Contents
Top Machine Learning Frameworks
TensorFlow is an open-source Machine Learning platform that encompasses a robust ecosystem of tools, libraries, and resources for fast numerical computation using data flow graphs. It has a simple and flexible architecture that facilitates easy development of state-of-the-art ML models and experimentation. Read more about Tensorflow.
The data flow graphs can process batches of data (“tensors”) using a series of algorithms described by a graph, wherein the data movements through the system are termed as “flows.” this is how TensorFlow gets its name.
TensorFlow allows for easy development of ML models. You can even train and deploy your ML models anywhere. Furthermore, the tool lets you assemble the graphs either in C++ or Python and process them on CPUs or GPUs.
Theano is a one of the popular Python libraries designed to help developers define, optimize, and evaluate mathematical computations comprising multi-dimensional arrays. It was developed at the LISA lab to facilitate fast and efficient development of ML algorithms.
Theano boasts of excellent integration with NumPy and leverages GPU to perform fast data-intensive computations. Apart from this, Theano features an efficient symbolic differentiation and enables dynamic code generation in C.
Caffe is a Deep Learning framework. It is one of the open-source deep learning libraries. While it is written in C++, it has a Python interface. The core idea behind this combination was to promote expression, speed, and modularity. Caffe was developed at the University of California, Berkeley.
Caffe is the fastest framework for the development of Deep Neural Networks. It has an expressive architecture that allows for innovation, while its extensible code encourages active development.
It sports a well-structured Matlab and Python interface and enables you to switch between CPU & GPU with setting a single flag to train and deploy to commodity clusters. Another benefit is that Caffe doesn’t require any hard coding for defining models and performance optimization.
Scikit-Learn is an open-source, Python-based ML library designed for ML coding and ML model building. It is built on top of three popular Python libraries, namely, NumPy, SciPy, and Matplotlib. Scikit-Learn has the best documentation among all the open-source libraries.
Scikit-Learn is loaded with a wide range of supervised & unsupervised ML algorithms like k-neighbours, support vector machine (SVM), gradient boosting, random forests, etc. The tool is highly recommended for data mining and statistical modelling tasks.
5. Amazon Machine Learning (Amazon ML)
Amazon ML is a cloud-based service that encompasses the most extensive range of ML and AI services for businesses. It is equipped with a host of visualization tools, wizards, and pre-trained AI features that help you build intuitive ML models from scratch, without spending tons of time in understanding the intricacies of complex ML algorithms.
With Amazon ML, developers of all skill levels can learn how to use and handle various ML tools and technologies. It can connect to the data stored in Amazon S3, Redshift, or RDS, and run binary classification, multiclass categorization, or regression on the data to develop ML models. While you can custom-build ML models by leveraging open-source frameworks, you can also use the Amazon SageMaker to quickly build, train, and deploy machine learning models at scale.
H2O is an open-source ML platform. It leverages math and predictive analytics to find solutions to some of the most challenging business issues in the modern industry. It combines several unique features that are not currently found in other ML frameworks such as Easy-to-use WebUI and Familiar Interfaces, Best of Breed Open Source Technology, and Data Agnostic Support for all Common Database and File Types.
H2O lets you work with your existing languages and tools while also allowing you to extend seamlessly into Hadoop environment. It is highly business-oriented and promotes data-driven decision making. The tool is best suited for predictive modelling, risk and fraud analysis, insurance analytics, advertising technology, healthcare, and customer intelligence.
7. Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit (formerly known as CNTK) is a toolkit offered by Microsoft to help developers harness the intelligence hidden within large datasets by leveraging Deep Learning technologies.
The Microsoft Cognitive Toolkit aids neural networks to sift through vast and unstructured datasets. It is highly compatible with numerous programming languages and ML algorithms and provides scaling, speed, and accuracy of commercial-grade quality. With its intuitive architecture, it reduces the training time significantly. Also, it allows you to customize it by choosing the metrics, networks, and algorithms as per your requirements.
8. Apache Singa
SINGA, an Apache Incubating project, is a general distributed Deep Learning platform for training Deep Learning models. Its design is that of an intuitive programming model based on the layer abstraction. SINGA has a flexible architecture for promoting scalable distributed training.
It supports a variety of popular Deep Learning architectures including Feed-Forward Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and even energy models like the Restricted Boltzmann Machine (RBM).
There you go – we’ve named for you some of the top-performing and widely used ML frameworks in the world. Now it’s your turn to try these out for your next ML model and application. The best part is that each tool comes with unique features that make Machine Learning much more fun and exciting.
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Are Caffe and Caffe2 two different frameworks?
Due to its unrivalled speed and well-tested C++ codebase, the original Caffe framework was ideal for large-scale product use cases. Caffe2 is a deep learning framework that makes it simple to experiment with deep learning and leverage new models and algorithms offered by the community. With Caffe2's cross-platform frameworks, you can scale your ideas leveraging the power of GPUs in the cloud or to the masses on mobile.
Is Keras a framework based on Python?
Keras is a high-level neural network Application Programming Interface (API) written in Python that simplifies debugging and studying neural networks. This open-source neural network toolkit is based on CNTK, TensorFlow, and Theano and may be used to experiment with deep neural networks fast. Its API is high-level, user-friendly, modular, and extensible, allowing for fast experimentation. Keras is a programming language that can be used on both the CPU and the GPU.
What are the limitations of using Tensorflow?
If you are looking for a fast framework, Tensorflow is not the right choice as it lacks speed. Debugging is a little complex too, due to its unique structure. One needs to have a good knowledge of calculus and linear algebra in order to use Tensorflow.