Artificial Intelligence for IT Operations

Artificial Intelligence for IT Operations

AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.
In the recent past, Artificial Intelligence and alternate data analytics have emerged as powerful ammunition in the arsenal of the firms fighting to generate alpha and reduce costs. Companies such as Google, Facebook, Microsoft, and others are also investing heavily in AI research, and the results are quite evident. The release of self-driving cars is just an example of rapid AI growth.
The rapid development of AI worldwide and its pervasiveness in everyday life has created some anxiety among the public. And it is not just in the form of walking and talking robots. The rise of AI has pervaded areas ranging from healthcare, retail, transport and banking to food and beverage as well as dating services.

Examples of AI Technology

  • Machine learning is the science of getting a computer to act without programming. It is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.
  • Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics.
  • Pattern recognition is a branch of machine learning that focuses on identifying patterns in data.
  • Automation is the process of making a system or process function automatically. Robotic process automation, for example, can be programmed to perform high-volume, repeatable tasks normally performed by humans
  • Machine vision is the science of making computers sees. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is used in a range of applications from signature identification to medical image analysis.
  • Natural language processing (NLP) is the processing of human — and not computer — language by a computer program. One of the older and best known examples of NLP is spam detection, which looks at the subject line and the text of an email and decides if it’s junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition
  • Robotics is a field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used in assembly lines for car production or by NASA to move large objects in space.

AIOps (Artificial intelligence for IT operations)

AIOps is an umbrella term for the use of big data analytics, machine learning and other artificial intelligence technologies to automate the identification and resolution of common IT issues. The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor assets and gain visibility into dependencies without and outside of IT systems.

An AIOps platform should bring three capabilities to the enterprise:

  • Automate routine practices
  • Recognize serious issues faster and with greater accuracy than humans
  • Streamline the interactions between data center groups and teams

Business Benefits of AIOps

  • Insights into workloads that drive costs
  • Eliminating the skills gap
  • Provide application availability and eliminate customer frustration
  • Avoid costly service disruptions and eliminate firefighting
  • Increase business responsiveness

Drawbacks of AIOps

  • The cost incurred in the maintenance and repair of this system is very high. Programs need to be updated to suit the changing requirements, and machines need to be made smarter.
  • Machines may not be as efficient as humans in altering their responses depending on the changing situations.
  • AIOps demands trust in tooling, which can be a gating factor for some businesses.
  • For an AIOps tool to act autonomously, it must follow changes within its target environment accurately, gather and secure data, form correct conclusions based on the available algorithms to match business priorities and objectives.

Humans program AI to make the smartest and most consistent decisions, but if AI meets an unexpected condition like when should the self-driving car stop to let a police vehicle through or an ambulance, it will not always do the right thing. So, human and machine must work together.

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