Enhancing Quality Ascpects of ML in Official Statistics with the right ML OPs Framework
Florian Karl* 1
Abstract
The increasing complexity of machine learning models poses significant challenges in managing their lifecycle from development to deployment and maintenance. Machine Learning Operations (MLOps) has emerged as a crucial building block in machine learning application to ensure efficiency, scalability and reliability of these models, while also maintaining the reproducibility of results. In official statistics, the application of machine learning introduces unique requirements, particularly focusing on rigorous quality standards for transparency and reproducibility. With the increased adoption of machine learning across tasks like nowcasting or natural language processing, MLOps has become a topic of interest for official statistics. This talk aims to explore the specific requirements of MLOps within the context of the Federal Statistical Office of Germany (Destatis). We delve into the diverse needs of various user groups within Destatis considering constraints related to data security, compatibility with existing tools, support for relevant programming languages, and the integration of open-source solutions. We further present a proposition for an MLOps architecture tailored to meet these unique circumstances and substantiate our architectural choice by providing a brief overview of its functionality.