Deep Learning and AI

Top Machine Learning Frameworks

August 25, 2021 • 10 min read

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What Machine Learning Framework Should You Use?

Artificial Intelligence (AI), Deep learning (DL) and Machine Learning (ML) have advanced quickly over the last few years and are proven transformative engines for companies developing new technologies to get faster results and make our lives easier. 

The industry itself has grown rapidly and as the popularity of DL and ML continues to solidify, choosing the right framework is an important decision, and perhaps a critical one, to remaining competitive and improving business.

The number of frameworks available to data scientists and developers initially increased in the early years of AI, but many have lost out to the most popular ones: TensorFlow and PyTorch. As more support has grown around these two frameworks, smaller niche frameworks and libraries have popped up that work directly with one or both of the main standouts. 

This article will take a look at TensorFlow and PyTorch, as well as a couple other frameworks that are still supported.

What’s a Machine Learning Framework?

A machine learning framework is an interface which allows data scientists and developers to build and deploy machine learning models faster and easier. 

There are many ML frameworks out there, each helping users to define ML models in precise, transparent and concise ways . These frameworks rely on complicated algorithms and unless you’re a ML expert, these algorithms are very hard to understand. That's why frameworks have become so popular! It makes more sense to save a ton of time by using a well documented and supported framework, instead of developing and maintaining your own.

Best Machine Learning Frameworks

Every data scientist has their own favorite ML framework. Still, choosing which framework to use depends on the work you’re trying to perform. The following is a short list of the most popular frameworks that will help you become a better scientist/researcher/engineer!

1. TensorFlow

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OVERVIEW: TensorFlow is an open-source, JavaScript library and one of the widely used Machine Learning frameworks. Being Open-source, it comes for free and provides APIs for developers to build and train ML models.

From the stable of Google, TensorFlow is a versatile and possibly the better one among Machine Learning tools. It provides extensive, flexible features, an exhaustive library for programming, classifications, regression models, neural networks, including a suite to write algorithms for software. It runs on CPUs and as well on GPUs. The key advantage of TensorFlow is that it can be used in two ways, Script Tags or by installation via NPM. The challenge with TensorFlow is that it is not learner-friendly for beginners.

TensorFlow is an end-to-end open source platform for machine learning (ML). It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

What's more, it covers a number of use cases on top of the standard TensorFlow library:

  • TensorFlow.js - a JavaScript library for training and deploying models in the browser and on Node.js.
  • Tensorflow Lite - a lightweight library for deploying models on mobile and embedded devices, which is a big advantage over any other framework if you are looking to work on mobile platforms.
  • TensorFlow Extended - an end-to-end platform for preparing data, training, validating, and deploying models in large production environments.

The tensorflow.org website helps beginners get started with any of these, as well as a ton of resources, case studies, TensorFlow datasets, an engaged community, and simple step-by-step walkthroughs to solve common machine learning problems with TensorFlow.

Based on your skill set, these include:

TensorFlow is definitely the go-to framework for people looking to take projects from concept to production, allowing you to rapidly deploy models using TensorFlow Serving. It's been around for a long time and has extensive documentation and support from Google researchers, as well as a much larger community and helpful content that has been developed over the years.

TensorFlow also offers better visualization tools, including TensorBoard - a suite of web applications for inspecting and understanding your TensorFlow runs and graphs.

If you're ready to get started, you can start by installing TensorFlow!

To install TensorFlow 2, visit: https://www.tensorflow.org/install

If you need to hone your machine learning skills first, the TensorFlow website offers a lot of great resources to learn: https://www.tensorflow.org/resources/learn-ml

2. PyTorch

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OVERVIEW: PyTorch Machine Learning Framework, which is based on Torch and Caffe2, comes with loads of options for optimizing algorithms. The torch is perfectly suited to design neural networks using Autograd Module and Natural Language Processing. PyTorch is open-source and supports cloud-based software development. It comes with multiple features, including libraries, tools, and distributed training. For User Interface development, it supports Lua language. Pytorch is used by Facebook and IBM. The torch is rated as the best for designing computational graphs, and its front end development is hybrid, which makes usability very versatile. The platform was designed to be integrated with Python and compatible with libraries such as Numba and Cython

PyTorch is an open source machine learning framework that accelerates the path from research prototyping to production deployment.

PyTorch, on the other hand, is more Python-based (hence the name which is a combination of Python and Torch). So, if you use Python regularly then you might find PyTorch familiar and easy to use.

PyTorch has found popular support with researchers, looking for quick solutions and who are focused on smaller-scale models. More specifically, people working on text and language processing (NLP), as well as computer vision, tend to lean towards PyTorch, since it is has been fully adopted by Facebook who use it for those specific purposes.

PyTorch also includes TorchServe, an easy-to-use tool for deploying PyTorch models at scale. It is cloud and environment agnostic and supports features such as multi-model serving, logging, metrics and the creation of RESTful endpoints for application integration.

Regarding mobile support: although TensorFlow currently excels with mobile support, PyTorch is building out their own support and will be looking to catch up to its competition in the near future.

You can still use TensorFlow for just about anything you use PyTorch with, and vice versa, and many people use both in their work depending on what project they are working on. In the end, it's more a matter of preference.

If you're ready to get started, you can start by installing PyTorch!

To install the latest version of PyTorch, visit: https://pytorch.org/get-started/locally/

Like TensorFlow, PyTorch also includes a large number of resources on their website: https://pytorch.org/resources/

  • Comprehensive documentation
  • Tutorials
  • Thriving community
  • Interactive Deep Learning eBook
  • fast.ai free learning courses
  • Examples
  • Slack discussion channel
  • And more

3. Sci-Kit Learn

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Sci-Kit Learn specifically supports development work in Python with an extensive library for Python programming language. It is rated by users as one of the best for data mining and data analysis. 

Sci-Kit Learn provides support for designing algorithms and models for classifications, clustering, pre-processing, regression, Dimensional reduction, and Model selection.

Sci-Kit Learn is an open-source platform, and it comes for free. It is learner-friendly even for amateur developers and comes with detailed documentation. Furthermore, it permits developers to change any pre-set parameters for algorithms when in use or during run-time. The framework is better suited for unsupervised and administrative calculations.

4. Apache MXNet

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Apache MXNet  is an open source deep learning framework suited for flexible research prototyping and production.

It was adopted by Amazon as its basic Machine Learning tool for AWS. It is distributed on a cloud infrastructure via a parameter server. It is scalable across several GPUs and servers. Also, MXNet supports multiple language APIs, which includes Python, JavaScript, Julia, C++, Scala, and Perl.

Key features include:

  • Hybrid Front-End - A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed.
  • Distributed Training - Scalable distributed training and performance optimization in research and production is enabled by the dual Parameter Server and Horovod support.
  • 8 Language Bindings - Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl.
  • Tools & Libraries - A thriving ecosystem of tools and libraries extends MXNet and enable use-cases in computer vision, NLP, time series and more.

Several IT companies support Apache MXNet, and that includes Microsoft, Intel, and Baidu. Some of the leading research and educational institutions, such as the University of Washington and MIT, also use Apache MXNet extensively.

Conclusion

Machine Learning and its frameworks have definitively set the stage for automated systems. What we’ve seen in this article has barely touched the surface, and though machine learning isn’t new to innovation we’ve seen an exponential rise in the past few years. 

The focus lately has been on significant performance breakthroughs in speech and language recognition, image processing and computer vision. Although there are many other frameworks than the ones listed above, our goal is to provide some insight into some of the more popular ones.

Thanks for reading, and happy training!

Let us know about any topics you're interested in by dropping a comment below, or feel free to contact us at any time with questions you have.


Tags

machine learning

ml

ai

deep learning

tensorflow

pytorch

sci-kit learn

mxnet

python



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