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MLSecOps is Fundamental to Robust AISPM // Sean Morgan // #257

MLOps.community

CHAPTER

Securing MLOps: Balancing Innovation and Safety

This chapter focuses on the critical oversight of security in MLOps compared to traditional DevOps, emphasizing the need for proactive security integration throughout the model development lifecycle. It discusses the complexities related to data management, the risk of utilizing tainted datasets, and the security implications of AI models. The speakers advocate for a collaborative approach between security and ML teams to address vulnerabilities and ensure the integrity of machine learning operations.

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