Free and Open Machine Learning¶
This publication is created to promote and advocate the use of FOSS machine learning for real practical business use cases. Machine learning is a fascinating technology. Free and Open machine learning should be the norm for business innovation. So simple to use for complex problems. Freedom to control machine learning technology is not self-evident. Free and Open Machine Learning puts you in full control.
This publication empowers everyone to make a head start using the powerful machine learning technology in a Free, Open and Simple way.
Machine learning is an exciting and powerful technology. The continuous use and growth of machine learning technology opens new opportunities. It also enables solving complex problems in a simple way. Problems that are impossible to solve by using traditional software technologies. This great machine learning technology should available for everyone. This means that everyone should be able to learn, play and create great applications using machine learning technology. But also reuse existing machine learning solutions, inspect solutions and improve solutions of others. Without borders or strings attached.
The key focus of this publication in on Free and Open Machine Learning technologies. This to remove barriers for learning, playing, using and reusing machine learning technologies for real practical use cases for everyone.
Of course you can use or switch to cloud company solutions to deploy your machine learning driven application in production. But besides vendor lock-in, crucial aspects like safety, privacy and security for machine learning applications are only possible when using fully transparent Free and Open machine learning building blocks and solutions.
This document describes an open machine learning architecture. Including key aspects that are involved for real business use. This means e.g. that we focus on FOSS machine learning software and open datasets.
Since the majority of humans are not a graduated mathematician, we skip deep mathematical background concepts of machine learning algorithms in this publication. Good books with lots of mathematical background information on how machine learning works are available for more than 70 years. There are plenty excellent free and open publications available if you want to learn everything about the inner working of the mathematical algorithms that power the current exciting machine learning applications. In the learning resources section in this publication you can find a list of good references. All references in this publication are publications available under a creative commons license (cc-by).
This publication has a core focus on outlining how Free and Open machine learning can be used for real business use cases. This is done by describing:
- Key machine learning concepts. The focus is on concepts that are needed in order to use solid FOSS machine learning frameworks and datasets when creating a machine learning powered application.
- An open reference architecture for creating and maintaining a machine learning solution architecture and IT landscape.
- Presenting useful and most used FOSS machine learning building blocks. Most Open Solution Building Blocks for machine learning are FOSS based. The most used solutions have a healthy ecosystem of (open) tools and service companies that enables you to create your machine learning application as fast as possible.
- Key quality aspects for engineering and maintaining your machine learning application.
- Important safety, privacy and security aspects to prevent disasters.
- Ethical issues (like bias) and guidelines for handling these issues in a transparent way.
No pieces of program code or mathematical formulas is presented in this publication. The emphasis is on machine learning concept and applying machine learning technology for real business use cases. No programming knowledge is needed to enjoy and learn machine learning.
This publication is created to give you a head start with using Free and Open machine learning technology to solve your business problems. Without any strings attached, so the focus is on Free and Open transparent machine learning technologies and solutions only!
Table of Contents¶
- Why Free and Open Machine Learning
- What is machine learning
- ML, AI and NLP: What is what
- The paradigm shift: Creating smart software
- Overview machine learning methods
- Other common terms used in the ML world
- Machine Learning for Business Problems
- When to use machine learning?
- Common business use cases
- Business Examples
- Business Challenges
- Business capabilities
- Business ethics
- ML Reference Architecture
- The machine learning process
- Architecture Building Blocks for ML
- ML Reference Architecture
- Security,Privacy and Safety
- Natural Language Processing
- ML implementation challenges
- Building Blocks for FOSS ML
- Open Machine Learning Frameworks
- ML Frameworks
- Analytics Zoo
- Apache MXNet
- Apache Spark MLlib
- Microsoft Cognitive Toolkit (CNTK)
- NNI (Neural Network Intelligence)
- TF Encrypted
- Vowpal Wabbit
- Computer vision
- ML Tools
- ML hosting
- NLP Frameworks