Even though advanced ML algorithms effectively tackles data privacy concerns through decentralized device-based model training, the DevOps principles are what elevate the end-to-end infrastructure to gain significant advantages by full operationalization.
This topic highlights that integrating DevOps best practices into the development, deployment, and management of ML-based systems can contribute to the overall efficiency, reliability, and security of the solution, even when the ML algorithm itself is designed to uphold data privacy.
Privacy-Preserving Machine Learning algorithms: Introduce the innovative realm of ML algorithms designed to uphold data privacy. Briefly refer to the characteristics of privacy-centric models and explore how they maintain the confidentiality of raw data.
Integrating DevOps Principles: Highlight the need of DevOps in enhancing the privacy and security of ML systems. DevOps principles contribute to the seamless operation and scalability of ML algorithms, when data privacy is a primary concern.