# Introduction

Machine learning (ML) is a rapidly advancing technology, made possible
by the Internet, that already has significant impacts on our everyday
lives. With the use of Machine learning you can solve challenging
problems that impact everyone around the world. Machine Learning (ML)
and Artificial Intelligence (AI) are rapidly emerging technologies that
have the potential to change our world with speed that humankind has
never experienced before.

Machine Learning and Artificial Intelligence are not the same, although
the current technologies developed for ML do help research and
developments on AI. ML can be characterized with a stricter definition
from an engineering perspective. Trying to define AI raises more
philosophical discussions on what intelligence is. This publication is
focused on Free and Open machine learning. But beware that the terms
machine learning and artificial intelligence are intertwined and many so
called AI applications are in fact driven by machine learning
technology.

You should be aware of the commercial buzz and fads surrounding AI and
ML: Machine Learning, deep learning and a lot of tools developed are not
\'a universal solvent\' for solving all current problems. There is no magic
machine learning tool or method yet that can solve all your complex
challenges. Machine learning is just a tool to solve a **certain type**
of problems. Maybe in future the use of machine learning can be applied
to a broader landscape of problems than currently possible. But do not
try to solve all your problems with one (new)technology or toolset.

Artificial Intelligence and Machine Learning are now again in the
forefront of global discourse, garnering increased attention from
practitioners, industry leaders, policymakers, and the general public.

But despite the hype and money invested in machine learning technology
the recent 5 years, one big questions remains: Can machine learning
technology help us to solve hard and complex business problems
like climate change, health welfare for all humans and other urgent
problems?

This publication gives you a reality check. You learn what is easily
possible using new machine learning technologies and tools, what the
current potential is and what still remains wishful thinking for the
future. We like transparency, so we focus solely on free and open
machine learning technologies.

![Hope and Hype](/images/hope-and-hype.png)

Innovation needs openness. This is also valid for machine
learning technologies. Without real openness new developments and
innovations in machine learning are impossible. As a practitioners in
your business domain and with your unique expertise you can start making
a difference. This publication gives you a starting point for trying to
apply free and open machine learning technology on your unique use
cases.

## What is covered in this book?


Nowadays many people are talking about the transformative power of
machine learning and how it will revolutionize the economy, but what
does that mean for your business and how do you start? How to get solid
independent advice to learn and how to apply machine learning? Can you
improve or disrupt your business using FOSS machine learning tools that
are widely available? This book gives you an introduction to get started
with applying FOSS machine learning.

Machine learning concepts are mostly taught by academics for
academics. That's why most learning material is dry and maths heavy. The
theory behind machine learning is great, but requires also a very deep
understanding of statistics and math. There is a large gap between
theory and practice. Practice counts, because in a practical business
context you want to determine if you can solve your problems with
machine learning tools. Or at minimum do a short and cost efficient run
to determine if a project has potential and more investments make sense.

To apply machine learning for real business use cases other skills
besides some feelings for statistics and math are required. You need
e.g. be able to have some knowledge about all typical IT things that are
still needed before you can make use of the new paradigm that machine
learning brings.

This publication is created for applying free and open machine learning
in practice for real world use cases. This is where the rubber meets the
road. So the core focus is on the \'How\' questions. Key concepts are
outlined and a conceptual and logical reference architecture for free and open
machine learning architecture is given. This to empower you to make use
of FOSS machine learning technology in a simple and efficient way.

The field of machine learning is making rapid progress. Do you know what
kind of applications for direct business use are already possible today?
Are you aware of the currently low entry barriers that exist, to take
direct advantage of machine learning? Is your knowledge of free and open
source solutions available in the machine learning eco system up to
date? How do you classify safety, security and privacy risk when using
machine learning? These and other relevant questions for using machine
learning in a business context are the foundation of this book.

Within the FOSS machine learning domain new toolsets, applications and
companies are being created on a daily basis. So it is difficult to get
a hold on what ML applications are viable, and which are a hype, fads or
simply a hoax. Especially when the terms ML and AI are intertwined. This
publication guides you through tangible working open source machine
learning software.

The mentioned FOSS machine learning software building blocks in this
publication are used at large. For real business use cases, and maybe
with large similarities for your use case. And because a lot of ML
software and tools needed is based on open source software(FOSS), solutions and tools
available can be studied and improved.

Given that machine learning tools and techniques are already an
increasingly part of our everyday lives, it is crucial for professionals
in the IT industry to gain more knowledge on machine learning. You
should start asking critical questions and maybe try to do some simple
experiments. What will you do with machine learning tools and
applications the coming 3 years? Are you really aware of the safety and
privacy concerns evolving that are part of this technology? Do you
really understand and control the working?

This publication is all about taking advantage of the new FOSS machine
learning technologies for your business. The major machine learning
concepts are explained, but the main emphasis of this book is to give
insights in the various possibilities that are available within the open
source machine learning ecosystem. This so you can start applying
machine learning in your business today, without hidden dependencies or
unknown strings attached towards a vendor or cloud hosting provider.

This publication gives an overview of all important FOSS machine
learning frameworks and FOSS machine learning support tools that you can
use for prototyping or for real business use cases and production
systems.

This publication does not explain and dive into the statistics and deep
mathematical algorithms behind machine learning. Also the algebra
functions that form the foundation under machine learning algorithms and
software libraries are only explained if needed for practical use and
experiments. If you are interested in learning the mathematical
foundations on which machine learning is developed, you can find good
free and open material in the reference section of this book.

This publication aims to cover the high level machine learning concepts
and gives you information to get started to work with free and open
machine learning for your business use case.

So this publication is concentrated on machine learning aspects where
software, business and technology touch each other.

![Domains touching](/images/domains.png)

(\* When we write Open Source Software or OSS in this report we
explicitly mean FOSS as defined by the Free Software Foundation -
FSF.org )

## Who should read this book?


This book is created for everyone who wants to learn and get started
with machine learning without being already forced into a specific
solution. Creating Machine learning applications is possible with the
use of FOSS building blocks only and on premise. So you do not need to
use directly expensive Cloud infrastructure or commercial software
packages. So if you like IT architecture, simple concepts and want to be
empowered to play with machine learning and create your own solution,
then this publication is for you.

This book is primary written with software developers, system
administrators, security architects, privacy controllers, IT managers,
directors, business owners, system engineers, quality managers, IT
architects and other curious people interested in open technologies in
mind.

This book crucial outlines machine learning concepts, but will not go
into mathematical or technical details. But after reading this book you
will have a more complete and realistic overview of the possibilities
applying machine learning (ML) for your use cases.

## Why another book on Machine Learning?


There are many books, courses and tutorials that teach you what machine
learning is. However most of these books and courses are focused on
hands-on learning and requires you to program. Also many books are
focused on explaining concepts without a clear focus on how tools can be
used to solve real business use cases. Also a publication that is truly open
and is focused on the broad landscape that is needed for Free and Open
Machine learning was simply not available.

Despite the enormous buzz and attention for machine learning it is
proven to be hard to apply machine learning for real profitable use
cases. Applying machine learning starts with understanding the core
concepts, business architecture needs, constraints and insights in the
technology components that are present. Also some notion of the typical
pitfalls and challenges for applying machine learning for business use
is needed.

## Is Machine Learning complex?


You might get the impression when visiting presentations from commercial
vendors that machine learning is simple. The hard work is already done
and all you have to do is get your credit card and make use of the
incredible machine learning cloud offering. This machine learning as a
service (MaaS) takes your company to the next level and the advise of
the sales consultant is clear: Using their MaaS service is so simple
that entering your credit card number is probably the hardest part.
Maybe it takes a minute, maybe more. But in the end you discover that
solving problems using machine learning is not that simple after all.
The great offerings of many large and small vendors selling MaaS from a
fantastic cloud offering do not solve your business problem in a simple
way. As with all new technologies and especially IT technology: There
are over promises on advantages and getting the return on your
investments is not simple. You are confronted with complex terminology,
a machine learning back-box from your vendor that is of course great at
billing, data collection and data cleaning problems you had never heard
of, and security, privacy and even safety issues. And if you think it
can not get worse also legal and ethical issues will slow your project
down.

By using an open approach (tools, methods, datasets) for machine
learning a lot of risks can be mitigated. E.g. it is easier to control
spending in the important ramp up phase of your project. If you need
more performance you can always move hosting to a cloud platform in a
later stage. But you need to start with a flexible and scalable
architecture that is no limitation for future goals.

There have been tremendous advances made in making machine learning more
accessible over the past few years. This publication outlines some great
OSS applications ready to be used, even if you really hate difficult
mathematical formulas. Multiple developments are in progress that now
really make it possible to drop your data and let a complex machine
learning algorithm do the hard work.

But don't be fooled. Even solving only \'some type of problems\' using
machine learning tools is a relatively 'hard' problem. So only equipped
with the right knowledge, tools and resources it is possible to get results. Solving soft business problems with machine learning
requires far more than a good computer scientist alone. Using machine
learning for soft problems requires a variety of disciples and a lot of
creativity, experimentation and tenacity.

## Organization of this book


The topics explored in this publication include:

-   Why Free and Open Machine Learning. This section outlines why we all
    should promote and advocate for openness and freedom regarding this
    promising technology.
-   What is Machine Learning. This is the section to read if you are
    short on time and want a simple outline of complex machine learning
    concepts.
-   Machine Learning for business problems. New technologies come with
    new opportunities for innovation. This section outlines common
    business use cases that are possible today using machine learning
    technology.
-   Machine learning Reference architecture. Starting with machine
    learning can be overwhelming. This section gives an overview of the
    business and technology aspects that you face when applying machine
    learning for real business use cases. But this section also helps
    you with developing your machine learning solution architecture.
-   Security, Privacy and Safety. The things you do not see are often
    the most important aspects. Security, Privacy and safety are very
    complex to deal with for normal IT solutions. But for machine
    learning these non functional aspects must be taken into your design
    upfront from a system perspective. This section outlines the key
    aspects for security, privacy and safety you should be aware of when
    creating machine learning applications.
-   Natural language processing (NLP). Hard to solve speech and text
    processing problems are now far more easily solved using machine
    learning algorithms. This section outlines still on of the most used
    applications for machine learning: NLP.
-   Machine learning implementation challenges: Knowing what machine
    learning can do and how it works is no guarantee that creating an
    machine learning application succeeds. The failure rate of normal IT
    projects are already very high for decades. Machine learning
    projects are complex and risky. This sections gives guidance on
    avoiding pitfalls when applying machine learning for real business
    use.
-   FOSS System Building Blocks for machine learning. This publication
    presents an opinionated list of FOSS software building blocks that
    can be used when creating machine learning applications. Starting
    with FOSS machine learning building blocks means you start with no
    strings attached. Switching to cloud hosting solutions later is
    always possible, but machine learning needs experimentation and
    playing. With open data and open tools.
-   Learning Resources. Some very good learning resources for machine
    learning and NLP are open. So licensed using a creative commons
    license. After reading this publication a next step can be to dive
    in depth into a specific machine learning aspect, framework or
    technology. This section provides references to open learning
    resources, including references to hands-on tutorials.

## Errata, updates and support


We made serious efforts to create a first readable version of this book.
However if you notice typos, spelling and grammar errors please notify
us so we can improve this publication. You can create a pull request on
github or simply send an email to us.

Since the world of machine learning is rapidly evolving, this book will be
continuously updated. That's why there is an open on-line version of
this book available that always incorporates the latest updates.

```{tip}
If like to contribute to promote the Free and Open Machine Learning
principles and to make this book better: Please **CONTRIBUTE!** See the HELP
section.
```