Machine learning is incomplete without open source technology – Learn Why!
The field of machine learning has been growing quickly by producing a wide range of learning algorithms for various applications. Since the cost of data storage has reduced and high-performance computers have become more and more accessible, a drastic growth in machine learning (ML) is seen in a large group of industries including healthcare, finance, manufacturing, retail, commerce, law enforcement, entertainment and more. Several open source tools are coming up that are capable enough to run inexpensive hardware and help individuals as well as organizations to perform excellent data crunching and predictive tasks.
Big players are giving away machine learning projects to the open source community
Tech giants have always been supporters of the open-source community and recently a lot of big companies have released much of their work to the public for free to explore, adapt and improve. However, a year earlier, some of the big endorsers of machine learning have shared their complete codebases with the community. Facebook revealed its optimized deep learning modules for Torch and another open source library Tensorflow was made open source by Google. Similarly, IBM open sourced its SystemML platform and Microsoft released the DMTK (Distributed Machine Learning Toolkit) for free.
Now these developments have clearly confirmed what researchers, startups or big organizations who are willing to use machine learning already knows. Moreover, the tech companies are no more considering algorithms and software as their valuable proprietary. In this day and age, the two things that matter is data and the ability to use the data.
Why organizations must implement machine learning
- Machine learning in general
- Why it is relevant more than ever
- Supervised Learning – features, Algorithms, Applications
- Unsupervised Learning – features, Algorithms, Applications
- Examples and strategies
- Feedback / questions
5 advantages of open source in machine learning projects
Reproducing scientific results and fair comparison of algorithms
Reproducibility of experimental outcomes is a foundation of science. In machine learning, numerical simulations are frequently used to provide experimental validation and comparison of methods. Preferably, such a comparison between methods is based on a rigorous theoretical analysis. Open source tools and technology offer an opportunity to thoroughly conduct research using publicly available source code without depending on the vendor. It also detects instances of scientific malpractice or fraud more easily since all the code necessary to perform the experiment is made available. Hence, making use of algorithms, including publicly available data and the source code can significantly support in the reproducibility and the viability of fair comparisons.
Quick bug finding and fixing
When you carry out machine learning projects using open source software, it becomes easy to detect and resolve bugs in the software. While not everyone will be able to properly fix bugs on their own, everyone has the access to inspect the source code, find out the issue and submit it to the maintainers of the project. The only challenge is the occurrence of software failure, other than that issues can be exposed and resolved much faster using open source technologies.
Accelerate scientific development with low-cost, reusing methods
It is a known fact that scientific progress is always made based on existing methods and discovery, and the machine learning field is not an exception. However, for the researchers, data scientists, and developers it is difficult to re-implement existing methods for testing and using on large projects or extend them. And, the complication of an existing method is usually so big that re-implementing its algorithms might need redundant efforts. This is why the availability of open source technologies in machine learning can leverage existing resources for research and projects greatly.
Long term availability and support
Whether it is an individual researcher, developer, or data scientist, whosoever, open source might serve as a medium to ensure that everyone is able to use his/her research or discovery even after changing the employer. This can be harmful to both employee and employer: obviously for the employee as he/she loses access to the tools they have been working with and for the employer as the piece of cryptic code becomes unsupported. Thus, by releasing code under an open source license increases the chances of having long-term support.
Faster adoption of Machine Learning by various industries
There are notable paradigms of open source software that has supported the creation of multi-billion dollar machine learning companies and industries. The main reason for the adoption of machine learning by researchers and developers is the easy availability of high-quality open source implementations for free. Moreover, if a method proves beneficial and its source code is available, it can be directly applied to the connected real-world problems in other fields or industries.
The final say
So far we have talked a lot about the implementation of the open source model in machine learning software which can be greatly advantageous for the growth of any business. Machine learning can indeed solve real scientific and technological problems with the help of open source tools. If you want to know how your business can leverage machine learning using open source tech, consult our experts.