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. Each of these data sources better lends itself to certain types of tasks,” Puri explained. “Time series data, for example, is more suitable for regression tasks, whereas unstructured data is best for classification tasks. Logs and other semi-structured data can be used for either of the tasks after suitable transformations.”
Watson AIOps also leverages semantic search techniques that can relate the current incident to past incidents, analyzing those contextual cases and suggesting possible next-best remediations. IBM AI innovations, like Watson OpenScale, are at the forefront of developing trusted and explainable technologies, and those innovations help SREs interpret the reason behind a Watson AIOps recommendation, which is critical to trusting those actions.
Coinciding with the launch of Watson AIOps are upgrades to Cloud Pak for Data, IBM’s big data and predictive analytics platform. IBM Planning Analytics, a product designed to automate business planning, budgeting, and forecasting, can now be added as an extension to Cloud Pak for Data, as can the on-premises mobile device management (MDM) service IBM InfoSphere Master Data Connect.
IBM today also released Accelerator for Application Modernization with AI, a suite of tools within IBM’s Cloud Modernization Service that aims to reduce the effort and costs associated with app modernization. Application Containerization Advisor taps AI to provide recommendations for containerization, considering 12-factor properties to measure complexity. Candidate Microservices Advisor automates the discovery of microservices from an app’s source code and data artifacts, capturing details like component entities, Java classes, and database tables. And Modernization Workflow Orchestrator employs AI-based symbolic reasoning to create modernization steps tailored to each app.
Elsewhere, IBM will soon roll out upgrades to IBM Cloud Pak for Automation, the company says, so that customers can more easily create automations that perform data capture, task automation, business routing, and other such tasks. As for IBM Watson Assistant, IBM’s AI-based customer service, it’ll gain a new feature called “autolearning” that draws on prior interactions to provide the best, most relevant answers to new questions on related topics.
5G and edge devices
Beyond AI and automation solutions, IBM today introduced dedicated IBM Services teams for edge computing and telco clouds — specifically 5G-enabled edge computing. And it announced the IBM Edge Ecosystem and the aforementioned Telco Network Cloud Ecosystem, which will seek to recruit equipment manufacturers, networking and IT providers, and software providers including Cisco, Dell, Juniper, Intel, Nvidia, Samsung, and others to help clients launch cloud and computing services.
The coalitions will make use of IBM’s new and existing edge and multicloud solutions, like IBM Visual Insights, IBM Production Optimization, IBM Connected Manufacturing, IBM Asset Optimization, IBM Maximo Worker Insights, IBM Visual Inspector, IBM Edge Application Manager, and IBM Telco Network Cloud Manager. Edge Application Manager, which is built atop the Linux Foundation’s Open Horizon software project, enables management of up to 10,000 edge nodes simultaneously for AI and analytics applications. Meanwhile, the OpenShift-powered Telco Network Cloud Manager handles automation capabilities to orchestrate virtual and container network functions both on OpenShift and the Red Hat OpenStack cloud platform.
Thomas anticipates that the adoption of 5G will make evident the utility of edge computing. 5G low latency makes it an ideal fit across scenarios like emergency response, robotic surgery, and connected-vehicle safety features, he asserts, because it shaves off crucial milliseconds that would be otherwise be spent sending workloads to the cloud.
“The convergence of 5G, edge computing, and hybrid multicloud is redefining how businesses operate — speeding innovation, creating better user experiences, and improving employee and customer engagement,” added Thomas. “IBM is helping enterprises capitalize on the opportunities created by this new computing model, offering a comprehensive set of open edge solutions and a robust open ecosystem. By drawing on our acquisition of Red Hat and industry expertise, we’re helping enterprises realize the opportunities of edge computing.”
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