The fourth industrial revolution is a concept that has become part of our daily vocabulary, especially for those of us who live in the world of technology and try to look ahead. Why the fourth industrial revolution? The first industrial revolution eliminated the barriers of energy with the steam engine; the second triggered productivity with the appearance of electricity and mass production chains; the third improved sustainability and quality thanks to the use of electronic technologies. Today, the fourth industrial revolution is combining ubiquitous connectivity and massive data to improve both efficiency and productivity.

Thus, an Industry 4.0 is an industry equipped with technologies that facilitate knowing what is happening in each of its processes in real time, connected among them, thus allowing all this data to become a valuable source information. This is based on the combination of three enabling technologies: Industrial Internet of Things, Cloud Computing and Big Data / Machine Learning, as described below.

First of all, the Internet of Things is based on the use of sensors, embedded systems and wireless communications for the ubiquitous and massive acquisition of data. Secondly, Cloud Computing is based on the provision of computer services through the Internet, facilitating the integration of data and guaranteeing the scalability of the systems. Finally, Big Data / Machine Learning is a concept that refers to the application of artificial intelligence algorithms on large amounts of data to obtain knowledge.

Bearing this in mind, the main objective of the course is to provide attendees with a practical vision about the advances in the field of Industry 4.0. Especially as regards the technologies related to the Industrial Internet of Things, Cloud Computing and Big Data / Machine Learning. To this end, the concepts associated with these technologies will be presented in a theoretical manner and practical exercises will be conducted to facilitate the students become acquainted with their actual operation and application in real scenarios.

Thus, at the end of the course the attendees will be able to understand all the elements that constitute these systems and how their integration is made, as well as the variables that condition their application in a real environment. In addition, at the end of the course the attendees will also be able to glimpse the changes in the business models derived from the application of these technologies in the field of industry.


This 3-day course is organized into eleven (11) 1.5-hour sessions that combine a theoretical approach with a hands-on methodology, allowing attendees to quickly grasp the general architecture and the implementation details of the Industrial Internet of Things, Cloud computing and Big data / Machine learning applications targeted at industrial scenarios.

In particular, the sessions will cover the following topics:

  • Overview of Industry 4.0 technologies and uses cases
  • Industrial communication technologies with TSN and 6TiSCH
  • Industrial communication protocols with OPC/UA and MQTT
  • Cloud computing platforms, including Amazon AWS, Google Cloud and Microsoft Azure
  • Big data and data analytics technologies and techniques, including platforms and algorithms

The main objectives of this Workshop are the following:

  • Understand the architecture and technologies behind Industry 4.0, encompassing sensors and data acquisition, communications technologies and protocols, cloud computing for data aggregation and big data/machine learning for data analytics.

Develop real applications that integrate embedded devices and communications with a cloud computing back-end using standard Internet protocols (based on OPC/UA and MQTT) and use the information to enable data analytics.


The Workshop is specifically designed for engineers and project managers working in industrial scenarios that are looking to:

  1. Understand how the Internet of Things applications are architected from one end (sensors) to the other (cloud computing) using standard communication technologies and protocols
  2. Understand how the large amounts of data generated by devices can be stored and used to obtain domain knowledge by applying big data and machine learning techniques
  • Mode: In person with supervised practices as complement to the theory.
  • Methodology: Keynote lectures and practical workshops.
  • Participants: A minimum amount of 5 and a maximum amount of 20.

Any topic or sub-topic of the workshop can be expanded and detailed in a second session tailored specially for the client. So the basic workshop can be supplemented with successive trainings if need it.

At the end the participant will get an USB PEN with a FREE distribution Linux with all the test tools used in the course.

  • One computer per student during the workshop.
  • Welcome stationery material (notebooks, pens, USB with additional information).
  • Training manual.
  • Certificate of successful completion.
  • Meals: Coffee break and lunch (Coffee, tea and refreshments available during the training).

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