In the IML4E project we are developing a systematic approach to support the European industry in setting up flexible end-to-end solutions for MLOps. We call this approach the IML4E framework.

The IML4E framework integrates the various technologies and methods developed in the IML4E project and allows the meaningful configuration of solutions for the development of industrial ML applications in different application domains. The framework consists of:  

  • A reference architecture for MLOps enterprise solutions that can be adapted to different application domains.
  • A set of one-off solutions for data quality assurance and efficient data preparation as well as continuous testing and monitoring that, alone or in combination with other techniques and methods, have the potential to make MLOps processes more efficient.
  • The IML4E methodology, that is a a set of Methods that describe how the one-off solutions should be used in the context of different MLOps activities.
  • Training materials that explain the IML4E methodology as well as the one-off solutions and allow developers to be trained.

IML4E Framework Overview
The IML4E Framework overview IML4E Consortium

To leverage the IML4E framework, we additionally provide an OSS platform compatible to our reference architecture, which consists of already available OSS components and provides the basic functional set of an MLOps process.

Finally, the IML4E experimentation platform represents a concrete instantiation of the technology stack, which can be used to experience the added value of the IML4E framework and its components by means of simple examples.

In summary, the IML4E framework will directly address the specifics of AI and ML by providing automation and reusability in the data and the training pipeline and will support continuous quality monitoring for different types of machine learning. IML4E will design the IML4E framework and its solutions in such a way that they

  • integrate seamlessly with existing best practices in software engineering, data science, and ML, 
  • fit in industrial settings, i.e., by means of case studies that heavily rely on AI and ML from relevant European industrial domains like e-Health, industrial IoT, invoicing operations, building automation, and consulting business,
  • are relevant European and international standardization bodies (e.g. DIN, ETSI, ISO) that deal with the standardization of methods dedicated to the development of AI and ML,
  • support the European software development and data science industry with open-source tools and frameworks dedicated to the development of AI.