During its annual IBM Think conference, IBM announced Watson AIOps, a new service that automates the detection and remediation of network anomalies.
Today during its annual IBM Think conference, IBM announced the launch of Watson AIOps, a service that taps AI to automate the real-time detection, diagnosing, and remediation of network anomalies. It also unveiled new offerings targeting the rollout of 5G technologies and the devices on those networks, as well as a coalition of telecommunications partners — the IBM Telco Network Cloud Ecosystem — that will work with IBM to deploy edge computing technologies.
Watson AIOps marks IBM’s foray into the mammoth AIOps market, which is expected to grow from $2.55 billion in 2018 to $11.02 billion by 2023, according to Markets and Markets. That might be a conservative projection in light of the pandemic, which is forcing IT teams to increasingly conduct their work remotely. In lieu of access to infrastructure, tools like Watson AIOps could help prevent major outages, the cost of which a study from Aberdeen pegged at $260,000 per hour.
“The COVID-19 crisis and increased demand for remote work capabilities are driving the need for AI automation at an unprecedented rate and pace,” said IBM SVP Rob Thomas in a statement. “With automation, we are empowering next generation CIOs and their teams to prioritize the crucial work of today’s digital enterprises — managing and mining data to apply predictive insights that help lead to more impactful business results and lower cost.”
Watson AIOps, which leverages semantic search techniques and which is built on the latest release of Red Hat OpenShift, runs across hybrid cloud environments and works with existing platforms like Slack, Box, and IT monitoring solutions such as Mattermost and ServiceNow. According to IBM Research chief scientist Ruchir Puri, it correlates among data sources to localize the root causes of issues and create an explainable diagnosis while recommending the best course of action.
“[AIOps’] algorithms… work with time-series data of metrics, semi-structured but voluminous data logs, structured data like alerts, and unstructured data in incidents and human conversations to automatically create a timeline of the evolving issue.
