The AI Engineer's Guide to Surviving the EU AI Act

Chapter 2. MLOps: A Proactive Compliance Catalyst

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This chapter aims to understand the importance of Machine Learning Operations (MLOps) as a set of processes for designing, developing, deploying, and maintaining AI systems. These systems are usually large software applications with embedded machine learning models. As covered in the previous Chapter, besides risk classification and requirements engineering for AI systems, a considerable part of the compliance process is establishing roles, processes, structures, engineering and MLOps practices required for AI Act compliance operationalization. Also, post-market compliance requires establishing MLOps practices like monitoring and alerting.

Hence, you need to get an overview of how to design ML-powered applications, what CRISP-ML(Q) is, and what the canonical MLOps is - the structured machine learning development process. It’s crucial to understand these pieces of designing, developing, and maintaining the AI systems to successfully implement ...