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The ITOA Landscape

ITOA Will Open the Door to the Value of Analytics for Ops 


Deep machine learning is expanding the automation and analytics capabilities of IT operations

Aligned with the simplification message in how IT management is conducted is the automation and analytics technologies. These two technologies have become synonymous with each other as the ability of machine learning, or artificial intelligence, has grown and become more applicable for specific use cases, such as ITOA or operational automation. One of the key areas where these two technologies are being deployed is in DevOps and the ingesting of code into a tool such as Jenkins. The ability of the machine learning and analytics can evaluate the code ingested, compare it to the last version of code and perform analysis on the degree of risk automatically releasing the code involves. This example is just one where the automation engine has no fixed rules on say the number of lines of code, number of database calls etc., but understands in the context of the code the degree of risk it represents.

The deep learning technologies in 2016 were boasted by the NVIDIA DGX-1, and AWS P1 instances, which made available powerful systems that could be used for applications to learn the behaviors needed cost effectively. Ovum expects that this democratization of the platforms will be the start of more complex deep learning solutions for functions that demonstrate a business value in terms of new business or efficiency savings. The result will be that ITOA will become a leading use case for these new solutions as ITOA has opened the door to the value of analytics for operational teams.


View all 2017 ITOA predictions here

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