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How to harness AI and machine learning models to optimize DevOps teams
August 29, 2019 Blog

 

Author: Rajalakshmi Srinivasan, Product Manager, Site24x7

Over the last few years, organizations have been building DevOps teams to improve application development for the cloud and to enhance business agility. This trend is seeing a sharp rise especially in APAC markets, according to a Forrester report, DevOps is gaining momentum in markets such as Japan, China, India, South Korea, and Singapore, as businesses believe it is key to catching up with the digital transformation wave and improve their business.

Digital transformation being a journey, organizations strive to build sustenance and have a seamless business continuity, but DevOps team often find lack of transparency due to disparate tools and data impeding this objective.

This calls for a continuously evolving system with application of logic and reasoning (AIOps) in identifying and fixing problems.

The value of AI and machine learning
AI in the recent times show the value, automation can bring to business processes and decision-making. However, the technology is limited by what humans program into it (e.g., garbage in, garbage out), and not to forget that AI is highly malleable. This brings the onus on businesses to ensure they manage and consistently modulate any AI solutions in their system.

Given the potential benefits of AI and machine learning, businesses should take note of the ways these powerful, emerging technologies can augment DevOps. Here are five ways monitoring tools laced with AI and machine learning technologies can benefit DevOps teams.

  1. Data correlation from individual silos: An app may consist of multiple microservices, and a single outage in just one of these components can have a cascading effect on the rest of the system – including credibility and cost. AI can help correlate past data across tools and formats, and provide unified feedback to help curtail such instances.
  2. Automating solutions to recurring issues: With the help of afeedback loop, AI and ML algorithms can be trained to deploy solutions automatically to check redundancy.
  3. Anomaly detection and intelligent alerting: AI’s ability to read vast sets of data helps in detectg anomalies in real-time and in slimming down the false alerts.
  4. Quicker response time with bots: AI-enabled chatbots can help reduce the mean time to respond (MTTR) in customer-facing scenarios by answering frequently asked questions.
  5. Fine-tuning deployment strategies: AI’s insights helps in no maintaining a fit and healthy application deployment life cycle in a variety of environments such as public, private and hybrid.

Weighing in customer expectations and user experience over processes, organizations no more consider the new technologies like AI and machine learning as a luxury but as a necessity in enhancing business efficiency and productivity.

No doubt AI holds enormous promises for organizations. It starts with automating mundane tasks , reducing costs and supplying an intuitive product. However, AI must be introduced thoughtfully, and any solution laced with AI needs to be monitored to ensure they become part of the silent backbone of an efficient system; not rogue actors whose actions require regular remediation.

As implementation of AI in businesses increase, AI tools will grow to be more productive and intuitive.

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