Lakhasly

Evaluation: This article highlights the need for ML models that are aware of the computing environment and suggests a trust evaluation system.It also points to the advantages of this approach, such as aa tamper-proof data history, increased trust through rating and certification, and accountability in the ML pipeline.In short, the case study analyzes key concepts related to ML pipelines, blockchain technologies and proposed a framework to securely leverage blockchain to address various security challenges of ML pipelines.The trust management system includes smart contracts, feedback mechanisms and Trust values updating regularly.????

Evaluation:
This article highlights the need for ML models that are aware of the computing environment and suggests a trust evaluation system. The trust values are determined by a number of attributes such as human expertise, reliability of infrastructure and credibility of data source. The trust management system includes smart contracts, feedback mechanisms and Trust values updating regularly. It also points to the advantages of this approach, such as aa tamper-proof data history, increased trust through rating and certification, and accountability in the ML pipeline. Additionally, future work includes measuring expert evaluation improvement certifications.
 The case study proposes a framework using private blockchain, sidechains and IPFS distributed storage.
 Certification of nodes, models and pipelines is done to establish trust and accountability.
 A reputation/trust system is outlined to rate participants based on their actions to further increase trust.
 Use of visual quality inspection case study to demonstrate how the proposed framework can mitigate known security attacks on ML pipelines.
In short, the case study analyzes key concepts related to ML pipelines, blockchain technologies and proposed a framework to securely leverage blockchain to address various security challenges of ML pipelines.