AI and ML Models Can Optimize DevOps

Over the last few years, companies 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 improving their business 

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

This calls for a continuously evolving system with the application of logic and reasoning (AIOps) in identifying and fixing problems.
Where AI and ML Come In

In recent times,  AI has shown the value that automation can bring to business processes and decision-making. However, the technology is limited to what humans programmed into it (i.e., garbage in, garbage out). Let’s not forget that AI is highly malleable. The onus is on companies to ensure they manage and consistently modulate any AI solutions in their system. 

Given the potential benefits of AI and machine learning, companies should take note of the ways these powerful, emerging technologies can augment DevOps. Here are five ways that monitoring tools featuring  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. It also impacts 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 for recurring issues: With the help of a feedback 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 detecting 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 maintaining a healthy application deployment life cycle in a variety of cloud environments, such as public, private, and hybrid. 

Weighing in customer expectations and user experience over processes, companies need to consider new technologies like AI and machine learning. It is no longer a luxury but a necessity in enhancing business efficiency and productivity.

No doubt AI holds enormous promises for companies. 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.

Rajalakshmi Srinivasan, product manager for Site24x7 at ManageEngine, contributed this article.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends.