Preface

We humans are since the beginning of the development of modern computers obsessed with creating computers that have super powers. Even before the birth of computers, research has been done on artificial intelligence (AI). The question what artificial intelligence really is, is hard and fuel for philosophical discussions.

Currently (as of writing 2020) we see more and more products developed that claim to have super powers that come close to AI. A look under the surface shows that the real progress on AI is made by a tangible technique, called machine learning. So our the focus in this book is on machine learning. And not on philosophical views on what will be in future possible when machine learning evolves towards AI.

Machine learning today is capable of solving challenging problems that impact everyone around the world. Problems that were impossible to solve for long or problems that where too expensive or too complex to solve. Now solving a certain type of complex problems is possible using new machine learning technology. Currently very complex problems and meaningful problems are solved using applications based on machine learning algorithms. Many firms involved are willing to tell and show you how easy it is! But you must be aware: machine learning is a buzzword in the industry! So the machine learning field is full of companies that use fads, all kind of vendor lock-in options and marketing buzz to take your money without delivering long running solutions.

This publication is aimed to give you solid information so you can start applying the new machine learning tools and frameworks. This publication enables you also with the knowledge of what is possible and what is still wishful thinking.

Everything in this publication presented is with no strings attached. So the focus is on openness for machine learning tools, algorithms and knowledge. The core focus is outlining core concepts and showing an open machine learning architecture that make machine learning possible for real business use cases. And of course this publication is focused on outlining open source solutions (FOSS) that make it possible to start your machine learning journey. The aim of this publication is to enable normal business IT consultants, IT architects and software developers to be a practical grounding in open machine learning and its business applications. This to help you to transform your organization into an innovative, efficient, and sustainable company for the future using new open machine learning technology.

Machine learning is and should not be the exclusive domain commercial companies, data scientists, mathematics, computer scientists or hackers. Every business and everyone involved with automation should be able to take advantage of the machine learning techniques and applications available. This is possible within the field of machine learning as you will learn in this publication.

Nowadays knowledge is more and more openly shared, thanks to open access, open publication licenses and open source software. So everyone can and should benefit from the possibilities that open machine learning frameworks and tools provide.

To create this publication a lot of papers, books and reports on machine learning have been examined. And doing crucial ‘hands-on’ to experiences and feel the power of machine learning algorithms turned out to be crucial for understanding and creating this publication as well. This publication is focussed on making a complex technology simple to use. In order to make things simple a deep dive in complex FOSS software was needed.

In the journey on learning how to apply machine learning for real business use cases many books turned out to be either too theoretical, or too much focused on programming machine learning algorithms. As an IT architect I missed the overall machine learning architecture picture from a typical IT architecture point of view. So business, information, application, infrastructure, security and privacy perspective. This publication fills up that gap.

Applying machine learning should be easy and simple. When barriers for using machine learning technology are lowered many more great applications will be developed for the benefit for everyone. This publication makes the complex field of machine learning frameworks, software and applications simpler to use for real business use cases. Creating meaningful machine learning applications in a already complex context is another discipline than creating and understanding the often complex machine learning algorithms. So this publication is for everyone who is short on time but is dedicated to make use of machine learning capabilities.

This publication is not an end, but is constructed as an continuous effort to provide usable open and non commercial information for applying machine learning technology. You can join this project too. See the HELP section in this book.

This publication was only possible with the help of you! If you have a suggestion or correction, please send an email to info [at] bm-support [dot] org. Ik will add you to the contributor list, unless you ask to be omitted.