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If we think of the newest trends in IT service automation, or try to follow the recent research, or listen to the tops speakers at conferences and meetups — they all will inevitably point out that automation increasingly relies on Machine Learning and Artificial Intelligence.

It may sound like the case when these two concepts are used as buzzwords to declare that process automation follows the global trends. It is partially true. In theory, machine learning can enable automated systems to test and monitor themselves, to provide additional resources when necessary to meet timelines, as well as retire those resources when they’re no longer needed, and in this way to enhance IT processes and software delivery.

Artificial Intelligence in turn refers to completely autonomic systems, that can interact with their surroundings at any situation and reach their goals independently.

However, most organizations are in very early days in terms of actual implementations of such solutions. The idea lying behind the need for AI and related technologies is that many decisions are still the responsibility of the developers in spheres that can be effectively addressed by adequate training of computer systems. For example, it is the developer who decides what needs to be executed, but identifying …

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