Open-source machine learning
Learn what open-source machine learning is and explore open-source machine learning projects, platforms, and tools.
Open-source machine learning is everywhere
From chatbots and image recognition to predictive healthcare and self-driving cars, machine learning is all around us and is becoming so deeply ingrained in our personal and professional lives that most of the time, we don't even notice it.
But behind the scenes, it's open-source machine learning that has enabled the rapid growth and evolution of machine learning frameworks and libraries, and thus made it possible for technology to become smarter and solve increasingly complex challenges for people and businesses.
What is open-source machine learning?
Machine learning has vastly grown in popularity in the last few years, with more companies finding ways to use AI to solve business challenges. As machine learning becomes more prevalent, it has also become easier to develop and implement, and that's largely thanks to free open-source machine learning software.
A growing number of technology companies have started making machine learning algorithms and software libraries available to developers at no cost, which has given those developers the ability to experiment with machine learning open-source projects.
With closed-source software, only one person or organisation owns it and can alter it, and users typically must sign a proprietary agreement that they won't do anything with the software that the owners didn't explicitly allow. Conversely, anyone can view, modify, and share open-source software, so users can alter the source code and bring it into their own projects.
You might be wondering why companies might be motivated to give away their software for free, especially when there's still a market for commercial software. But there are many advantages to this practise, even for large technology corporations.
For one, open-source machine learning platforms help spark innovation. The more developers who work with a particular tool, the better the chances are that someone will produce an ingenious idea for using or improving it. And more developers make it more likely that they’ll catch any bugs or issues that might pop up, allowing those problems to be resolved faster.
In addition, because open-source software is available to so many people, strong communities often develop around different open-source software projects, and these communities also contribute to the increasing evolution, popularity, and availability of open-source frameworks and libraries.
Advantages of open-source machine learning
Open-source machine learning tools can help companies solve a wide variety of technological challenges, and they offer the following advantages:
Open-source machine learning software is free and available to all, so naturally it draws a lot of users. As a result, if the software has a bug or something isn't working the way it should, it's likely to be caught and resolved quickly.
A large community of support
Because so many developers are attracted to open-source machine learning platforms, huge online communities grow around them. These communities offer forums that your developers can turn to when they encounter roadblocks or want to crowdsource opinions. Online forums usually have answers to most potential issues, and, as machine learning gains in popularity, the community's knowledge will continue to grow, too.
The chance to help accelerate acceptance of machine learning
While machine learning has tremendous power and countless possible use cases, some programmers might find it intimidating if they don't have a lot of experience with it. But developers are more likely to experiment with open-source machine learning because free software means that they have virtually nothing to lose in trying it out. And open-source software allows developers who have a wide range of skill sets to use it, which also increases the number of users.
Your developers might work on the same project for years. When they use open-source machine learning tools, they can have peace of mind that their skills and experience are portable. This makes these tools more appealing because they can use and improve on them with the reassurance that if their circumstances change, they won't lose all their contributions.
Open-source machine learning projects
Are you wondering what real-world use cases of open-source machine learning look like? Here are just a few examples of open-source machine learning projects:
As one of the most popular projects on GitHub, Style2paints allows users to easily colorize line art by using AI.
Deep voice conversion
Deep neural networks give you the ability to convert any voice to a target voice by using speech recognition and speech synthesis to imitate the voice of a celebrity or whoever you choose.
ONNX Runtime helps you accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms.
Responsible AI toolkits
Toolkits like Fairlearn, InterpretML, and Error Analysis enable you to gain deeper insights into the behaviours of your machine learning model, detect unfairness and errors, and find ways to build more responsible machine learning programmes.
DeepSpeed is a deep learning optimisation library that delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU.
OpenAI offers tools for developing, training, and comparing reinforcement learning algorithms.
MLFlow helps you manage all parts of the machine learning lifecycle—all the way from experimentation to deployment.
PyTorch offers training, tools, and libraries to help you build deep learning projects.
How open-source machine learning keeps companies competitive
When it comes to machine learning, open-source software can both make it easier to access and help foster a mindset of innovation, growth, and community in your company. When open-source machine learning platforms allow businesses to use and contribute to them, they create a feedback loop—an open place to share ideas, solve business challenges, and make products better and more user-friendly.
Additionally, by making machine learning more accessible, open-source machine learning platforms are helping accelerate AI adoption and acceptance among developers and businesses, which in turn makes AI better and smarter, benefitting everyone.
Open-source machine learning tools also help start-ups with awareness. When newer organisations offer open-source code to the public, it generates name recognition and fosters trust that might eventually lead to new users.
Choosing an open-source machine learning platform
What criteria should you take into account when evaluating open-source machine learning platforms? Consider the following factors:
If your project is time-sensitive, you'll want to take a platform's latency into account. How long will it take from the time you start the project until you see initial results? Look at streaming platforms if your project requires real-time results.
Ease of use
How difficult is the platform to install, configure, and use? Is it hard to programme? Does it have a flexible graphical user interface?
Here's where you'll want to take your team's programming experience into consideration. If coding isn't in your skill set, look at platforms that don't require much—or any—code-writing. Some open-source machine learning platforms are designed for experienced developers, but many simpler alternatives are also available.
Does the platform already have a large, active community of contributors? The more users, the more likely it is that additional resources will be developed to support the platform in the future.
Is the platform intended to be a standalone tool, or does it work with your organisation's other applications? Consider how you'd like to use the platform and whether it will interoperate with the applications or frameworks that you'd like to use. For example, one framework that Azure supports is PyTorch, a Python-first deep learning framework. Azure also supports many other applications such as TensorFlow and Scikit-Learn, but another platform might not support the frameworks that you need.
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