Abstract: The operation and maintenance of large scale production machine learning systems has uncovered new challenges which have required fundamentally different approaches to that of traditional software. The area of security in MLOps has seen a rise in attention as machine learning infrastructure expands to further critical usecases across industry.
In this talk we introduce the conceptual and practical topics around MLSecOps that data science practitioners will be able to adopt or advocate for. We will also provide an intuition on key security challenges that arise in production machine learning systems as well as best practices and frameworks that can be adopted to help mitigate security risks in ML models, ML pipelines and ML services.
We will cover a practical example showing how we can secure a machine learning model, and showcasing the security risks and best practices that can be adopted during the feature engineering, model training, model deployment and model monitoring stages of the machine learning lifecycle.
Bio: Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he contributes to policy and industry standards on the responsible design, development and operation of AI, including the fields of explainability, GPU acceleration, privacy preserving ML and other key machine learning research areas. Alejandro Saucedo is also the Director of Engineering at Seldon Technologies, where he leads teams of machine learning engineers focused on the scalability and extensibility of machine learning deployment and monitoring products. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has a strong track record building cross-functional teams of software engineers. He is currently appointed as governing council Member-at-Large at the Association for Computing Machinery, and is currently the Chairperson of the GPU Acceleration Kompute Committee at the Linux Foundation.