Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

August 2021

Robotic Data Automation (RDA): Reducing Costs and Improving Efficiencies of Your Log Management Investment

People’s involvement has been inevitable with log management despite advancements in ITOps. Log management at a high level collects and indexes all your application and system log files so that you can search through them quickly. It also lets you define rules based on log patterns so that you can get alerts when an anomaly occurs. Log management analytics solution leveraging RDA has been able to detect anomalies and aid predictive models over a machine learning layer.

Transform your Data Center with Confidence | Joint Webinar by CloudFabrix and Verge.io

Verge.io is partnering with CloudFabrix, a leader in artificial intelligence for IT operations, to chat about why software-defined everything is the way to go. This is a great opportunity to learn how to transform your current data center operations using the latest technology and intelligence. Here’s what we’ll cover: – How artificial intelligence and data center virtualization operating systems work together to change the thinking around traditional data centers.

RDA Addresses Top 5 Enterprise IT Challenges faced by CXO/IT Leaders

Robotic data automation (RDA) is designed to optimize IT functions for a broad spectrum. It is the next generation data automation technology that is specifically designed to bring efficiencies to multiple IT functions including CXO/IT leaders decision making, IT Ops, Helpdesk, Salesforce.com, ServiceNow and other applications used by enterprises.

Is Data the biggest barrier for AIOps adoption?

Much like oil, data that is available in a raw form is not useful at all. It has to go through various steps before AI/ML could touch it and derive valuable!! Check out the latest video by Shyam Sreenivasan, where he talks about DataOps and why you should automate your data pipelines to run AIOPS at scale.
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When Dominoes Fall: Microservices and Distributed Systems need intelligent dataops and AI/ML to stand up tall

As soon as the ITOps technician is ready to grab a cup of coffee, a zing comes along as an alert. Cling after zing, the technician has to respond to so many alerts leading to fatigue. The question is why can’t systems be smart enough to predict bugs and fix them before sending an alert to them. And, imagine what happens when these ITOps personnel have to work with a complex and hybrid cloud of IT systems and applications. They will dive into alert fatigue.