Which Machine Learning Framework Should You Use for Web Development

With the rate of machine learning and artificial intelligence advancing at such a rapid pace, web application development companies are starting to develop frameworks that allow them to implement these new technologies into their products and services quickly. With so many options on the market, it can be challenging to choose which one is right for your own development needs, especially if you don’t have much experience in this area yet yourself. Here’s a rundown of the top five machine learning frameworks available today, as well as their best uses and some popular applications built with them.

TensorFlow
Built by Google, TensorFlow is an open-source software library that you can use to build machine learning models. It supports deep learning (particularly image recognition) and large-scale computation. TensorFlow is likely your best bet if you’re building a web application and want to leverage machine learning. As of March 2017, there’s no way to run TensorFlow on Amazon Web Services (AWS). While it would be easy enough to create a virtual computer using something like Amazon Elastic Compute Cloud and Docker—and Docker supports both Windows Server 2016 and Linux—Google hasn’t released any instructions for doing so.

Scikit-Learn
Almost a decade old, Scikit-learn is one of the most popular machines learning frameworks today. It was originally developed by David Cournapeau in 2008 as part of a Google Summer of Code project and is currently maintained by Vincent Michel and Thomas Wiecki. Over time, it has grown to include a large number of supervised learning algorithms, including support vector machines (SVMs), regression models such as linear and logistic regression, decision trees, and classifiers like Naive Bayes.

Keras
Though it’s a young project, Keras has gained popularity because of its ease of use and high level of control over computational graphs. It also performs very well on large-scale image datasets such as ImageNet. For small applications and prototypes, Keras is recommended. If you choose to go with it, you can introduce deep learning and artificial neural networks with Python.
After you’ve gone through that tutorial (or if you already have experience), we recommend looking at our guide to implementing deep learning with Keras to put your new knowledge into practice!

Caffe
This is one of Google’s open-source neural network models. It has a flexible interface, allowing users to choose their own configuration parameters, which makes it great to build a custom model that isn’t a great fit for other frameworks. It doesn’t have its language but can be used with Python or C++.

Microsoft Azure ML
With Microsoft Azure ML, you can get started with machine learning in a matter of minutes. It’s simple, flexible, and you don’t need any background in machine learning to use it. This makes it an ideal choice for developers who want to experiment with machine learning but don’t have time or resources to develop their own framework from scratch. Best of all, Azure ML is free to use and has no limitations on data usage.

Conclusion
Choosing a Machine Learning framework is more than just picking a free, open-source library. It’s also choosing an entire technology stack. This can be daunting if you are new to web application development or machine learning altogether, but it doesn’t have to be! There are plenty of tools that make developing machine learning applications easier, and they don’t require any programming experience.

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