How to choose between machine learning frameworks? Machine learning has been around for quite a while now. In conjunction with artificial intelligence, it is perhaps one of the most useful concepts that has risen from the realms of science and technology in the past few years.
With machine learning gaining worldwide traction, budding developers and seasoned veterans alike are always looking to build innovative and useful machine learning projects. To do that, you need a framework, which is basically a library that contains tools to help you build your project. The choices are infinite. Do you need statistical analyses, or are you looking for linear algebra tools? Are you in for deep learning or neural networks? The answers to these questions will determine what kind of framework you should choose. Here are seven of the most popular machine learning frameworks that developers use around the world, in no particular order.
“Open source” is the word on almost every developer’s tongue right now. TensorFlow embodies the definition of open source, providing support for data-based programming across a multitude of tasks. Being highly flexible, it provides users with multiple machine learning models and versions, leading to easier migration between models and versions.
Another aspect of TensorFlow that attracts developers is that it is portable, i.e. runs on multiple platforms including mobile.
Torch, or PyTorch, is a scientific computing framework which is often touted as the easiest ML framework to develop with. It borrows its scripting language interface from the Lua programming language, offering high flexibility and speed. There are many pre-trained machine learning models available, which attracts giants of the tech world, such as Facebook AI research, and IBM.
3. CAFFE 2
The Convolutional Architecture for Fast Feature Embedding, or CAFFE, is another open source framework, developed by UC Berkeley. It is mainly written in C++, and focuses on deep learning applications such as image classification and segmentation.
Considering the different use-cases that ML is used for, CAFFE is definitely a blessing for the developers. For instance, companies that have been building business solutions for the pharma industry such as inventory management software or medical billing software, security and modular structure of data packets is important. The same thing would apply for any other kind of software too. To help with such issues while implementing Machine Learning, CAFFE 2 is the perfect solution.
CAFFE 2 is touted as the fastest ML framework for deep neural networks, and the interface is based on MATLAB and Python. The speed claims of CAFFE are indeed true, with it being able to process 60 million images per day with a single GPU.
A powerful clustering, regression and statistical modeling tool, Scikit-Learn is the go-to framework for developers looking to start building basic regression or classification models. It is integrated with many Python libraries, such as Numpy, Matplotlib, and SciPy. Supervised and unsupervised learning, along with cross-validation, are some of the prime features of Scikit-Learn. It is one of the most widely used frameworks for most practical and even complex tasks.
5. Apache Singa
Something for all the big data analytics enthusiasts out there- Apache Singa is a flexible and scalable ML framework, best suited for distributed training for big data. Popular deep learning models like convolutional neural networks, restricted Boltzmann machines, and recurrent neural networks are supported.
6. Amazon Machine Learning
A collection of tools to help all kinds of developers dabble in ML projects, AML is a framework which can help you create sophisticated and high end applications without having to learn all the deep nuances of how machine learning works.
Simply connect your AML framework to data stored in Amazon S3, RDS or Redshift, and begin running classification or regression on the data as per your requirements.
7. Apache Spark
Written in Java, Scala, R, and Python, Spark is a cluster computing framework which offers APIs for data scientists who are familiar with how R and Python work. Scalability, streamlined nature, and end-to-end compatibility are some of the salient features of Spark.
Looking to build your next ML project? Pick one of the seven frameworks that suits your application the most, and get coding!