In this webinar
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 use cases across industry.
What you’ll learn:
The key security challenges that arise in production machine learning systems
Best practices and frameworks that can be adopted to help mitigate security risks in ML models, ML pipelines and ML services
How to 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.
Which tools to secure production machine learning systems, as well as further the discussion around best practices reinforcing SecOps into MLOps.
Best practices on a critical area of machine learning operations which is of paramount importance in production.