Guideline for Deployment of Machine Learning Models for Predictive Quality in Production

Heymann, Henrik; Kies, Alexander D.; Frye, Maik; Schmitt, Robert; Boza, Andrés

doi:10.1016/j.procir.2022.05.068

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…One of the major issues in building resilient and robust systems in the biotechnology Industry 5.0 is deploying the machine learning pipelines securely. This involves considering any retraining of the models that might be required after releasing into the production lines [113]. Since machine learning models might need to be retrained on data continuously over time, some studies have proposed robust frameworks for continuous deployments of these ML models [114].…”

Section: Building Robust and Resilient Systemsmentioning

confidence: 99%

Integrating MLSecOps in the Biotechnology Industry 5.0

Pervez,

J. Titus

2024

The Role of Cybersecurity in the Industry 5.0 Era [Working Title]

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Biotechnology Industry 5.0 is advancing with the integration of cutting-edge technologies such as Machine Learning (ML), the Internet of Things (IoT), and cloud computing. It is no surprise that an industry that utilizes data from customers and can alter their lives is a target of a variety of attacks. This chapter provides a perspective on how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices. This chapter explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting how crucial it is to comply with current regulatory frameworks. With biotechnology Industry 5.0 developing innovative solutions in healthcare, supply chain management, biomanufacturing, pharmaceutical sectors, and more, the chapter also discusses the MLSecOps best practices that industry and enterprises should follow while also considering ethical responsibilities. Overall, the chapter provides a discussion of how to integrate MLSecOps into the design, deployment, and regulation of the processes in the biotechnology Industry 5.0.

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Section: Building Robust and Resilient Systemsmentioning

confidence: 99%

Integrating MLSecOps in the Biotechnology Industry 5.0

Pervez,

J. Titus

2024

The Role of Cybersecurity in the Industry 5.0 Era [Working Title]

View full text Add to dashboard Cite

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“…Data collection for the problem statement in Section 1 was conducted using an experimental design, documented in Mende et al, 10 employing a central composite design (CCD) methodology. As shown in Fig.…”

Section: Data Collectionmentioning

confidence: 99%

“…However, these models provided only a single output. Mende et al 10 shows the importance of process expert knowledge for feature selection for the explained NGM process. The process expert knowledge was crucial and provided more robust and close real-world interpretation for the feature reduction.…”

Section: Introductionmentioning

confidence: 99%