Vertex AI Foundations For Secure And Compliant ML/AI Deployment - cyberpogo

An increasing number of Enterprise customers are adopting ML/AI as their core transformational pillars, in order to differentiate, increase revenue, reduce costs and maximize efficiency. For many customers ML/AI adoption can be a challenging endeavor not only because of the broad spectrum of applications ML/AI can support, deciding on which one to prioritize can be a challenge, but because moving these solutions into production require a series of security, access and data assessments and features that some ML/AI platforms might not have. This blog post focuses on how to set up your Cloud foundations to cater specifically to the Vertex AI platform and its configuration to be able to set up proper Vertex AI foundations for your future machine learning operations (MLOps) and ML/AI use cases. Explainability is not covered in this blog post, but as a practitioner it is one of the key components for any production ready ML system to take it into account. You can take a look at Vertex Explainable AI for a more in depth approach on feature based explanations, feature attributions methods (Sampled Shapley, Integrated methods and XRAI) and differentiable and non-differentiable models.