Why focus on open source?ΒΆ

Open Source is an approach for the design, development, and distribution of new products & knowledge offering practical accessibility to its source. Real open source solutions have a license that is approved by the FSF.org or the OSI foundation. Open source is all about collaboration. Collaboration is key for developing, applying and using machine learning functionality.

Open Source Software(OSS) is the norm for machine learning. However using open source software will still be new and innovative for a lot of companies. However if you want really want to benefit from using machine learning software you must go for a solid OSS machine learning ecosystem. This makes you flexible, independent and you can still use thousands of consultancy firms and hosting companies that can help you.

A transition towards OSS can already be very hard and can be disruptive. And applying machine learning for real business cases is also already complex and challenging. But using machine learning without taken direct benefit from the OSS ecosystems that come with is like learning to swim without hitting the water. So hit the water as soon as possible, after a while you will see the benefits.

Machine learning applications are expensive to develop and to adopt. This accounts for the development process itself, but even more for the needed infrastructure and resources to develop meaningful applications for your business. This means that currently big firms like Google, IBM, Microsoft, Facebook and Amazon are at the front of the queue and smaller counterparts get left behind. Since most of the scientific knowledge is freely available and more and more infrastructure needed is available within the open source domain, this book is entirely focussed on open source. The technique behind ML is too much fun and often requires adjustments and tweaking, which is hard when you are using black-box solutions.

OSS developments in the machine learning field are no hobby projects. Almost all major OSS machine learning developments are backed by small or large companies(e.g. Google, Microsoft, Facebook, Uber) active in the deep learning ecosystem. Small machine learning OSS projects are often developed by researchers from backed by a strong foundation or by universities.

A focus on OSS for applying machine learning for real is crucial. OSS machine learning applications and frameworks have the following benefits:

  • Create solutions software faster, better and with less friction. You can adjust what you want without limitations.
  • Lower cost for creating your first pilot project. Mind: Your first attempts will fail. And the faster your pilot projects fail, the better. This since applying the new machine learning capabilities requires some learning curve. Technical, but even more on the organization and business side.
  • Flexibility and changeability.
  • No vendor lock ins. Of course the ML cloud offerings of the major tech companies are great (Azure ML, IBM Watson, Amazon, Google etc). But playing around without any strings attached and limitations set for you gives you a head start.
Popularity OSS Machine Learning

Almost all companies advertise with machine learning powered software products nowadays. This also means that all existing software that is already been sold for decades is now suddenly re-branded with the new machine learning buzz words. Like cognitive, artificial intelligence (AI) powered and data driven. You can easily be fooled since massive marketing effort (time, money, material) has been invested to sell the old buggy solutions as new innovative machine learning powered solutions. In reality black box solutions from small or large vendors are almost always based on fads. This is why you should be very suspicious when using cloud based machine offerings without an option to DIY. So if the new solution looks to good to be true, be aware. When using machine learning OSS solutions you can inspect yourself the working or ask someone to audit the software you directly know what the promise is based on. Because in the end: The security, safety and privacy of your customers are at risk.