What is it?
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 and reference processes 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.
- The IML4E maturity scheme, that is meant to identify the actual MLOps maturity of an organization and provides dedicated guidance for improvement.
- Training materials that explain the IML4E methodology as well as the one-off solutions and allow developers to be trained.
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.
Why is it necessary?
MLOps has established itself as a way to efficiently run machine learning in an industrial setting. However, MLOps adoption requires a significant investment of resources, expertise, and infrastructure.
However, MLOps implementation requires a significant investment in resources, expertise and infrastructure. Wrong decisions and false expectations can quickly lead to project failure. This is especially true for organizations that do not yet have an established DevOps and ML culture.
The IML4E framework supports organizations in implementing and improving their MLOps processes and capabilities by providing technical support, concrete training materials, and guidance on how to efficiently integrate ML into their own busioness and productions processes.
How does it work?
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. With the IML4E reference architecture and the OSS Platform it offers a fast entry into the world of MLOPS and allows companies to instantiate a MLOps pipeline with little effort.
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.