IML4E – MLOps Framework 

Machine learning (ML) and the use of software-based systems whose functionality is at least partially determined by ML are also becoming increasingly important in the European industry.  The IML4E MLOps framework is a collection of principles, methods and technologies designed to simplify the adoption of MLOps in enterprises.  It consists of a technology layer, called the “IML4E MLOps tools and techniques”, a methodology layer called “MLOps principles and methods”, and a platform layer called the “IML4E OSS platform and reference architecture”. All layers are supplemented by “IML4E Teaching material and playbooks”. The layers of the IML4E framework are shown in Figure 1.

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 MLOps 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. 


The IML4E MLOps 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 MLOps 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.