According to Gartner, Over 60% of machine learning models developed with the intention of operationalization were never actually operationalized.
In today's dynamic and competitive business environment, enterprises need to accelerate innovation and deliver faster time-to-value for their AI initiatives.
In order to do this, Data Science teams need easy access to a wide variety of ML/AI development tools, secured access to various enterprise data sources, and a repeatable standardized process to build, train, deploy and monitor models in production.
In this Masterclass, Victor Ghadban, Field CTO at Hewlett Packard Enterprise will discuss key considerations in terms of technology, organizational structure and best practices for success in your ML/AI journey.
Field Chief Technology Officer, AI & ML