In today’s dynamic marketplace, new applications that use data from multiple sources and deliver rapid insights constantly need to be created on very short notice. The challenge is how to have the flexibility to

rapidly develop and deploy new applications to meet fast-changing business requirements. The only way to ensure success is to use a dynamic architecture that delivers access to data, processing power, and analytics (including artificial intelligence and machine learning models) on demand.

Traditional development approaches break down. They do not offer the flexibility to easily incorporate new data sources or analytics. Nor do they lend themselves to today’s need for continuous changes after applications are deployed. Such problems become unmanageable as AI/ML needs expand throughout an organization.

There are two main approaches to address these problems. Businesses can either adopt an all-encompassing framework from a single AI/ML vendor or leverage the innovation of the AI/ML tools being developed in the open-source community. Both approaches benefit by leveraging a hybrid development and deployment model based on Kubernetes, the open-source system for automating deployment, scaling, and management of containerized applications.

#kubernetes #end-to-end #ai #ml