Is your data pipeline static?  Are you working with only fixed data sources with predictable data ingestion rate? Probably not?  Based on many customer discussions, the data pipeline is constantly changing to accommodate new data sources; hence, the data ingestion rate is often unpredictable.  The only thing that is certain is that more changes are coming.  For many IT teams, this uncertainly leads to a lack of clear requirements making it challenging to proceed.  Yet, it is in this dynamic environment that the enterprise needs to accelerate AI/ML deployment or risk having competitors leverage artificial intelligence and machine learning as a competitive advantage.

Unified, Scalable Architecture Enables Scaling at Your Own Pace

Here at Cisco, providing aunified, scalable architectureenables our customers to start small and then scale quickly.  For example, some of our big data customers started with just a few nodes of UCS and grown to thousands of servers within a cluster.  Having that scalable architecture has been the key for their growth.  It has been awesome to see that the Hadoop community evolved its architecture to be able to support containers and GPUs.  Hence, the big data scalable architecture is now able to support artificial intelligence and machine learning as well.  Cisco Validated Design with Hortonworks even details how to run TensorFlow from NVIDIA