June 29, 2022 By Mandy Long 2 min read

IBM is pleased to announce the latest release of IBM Cloud Pak® for Watson AIOps — version 3.4 — available as of June 30, 2022.

IBM Cloud Pak for Watson AIOps version 3.4 release focuses on improvements that continue to bring together our powerful IT Automation portfolio and provides enhancements to our scalability.

Additionally, we’ve added a new connector to our network monitoring product, IBM SevOne Network Performance Management (NPM), to help network data flow to Cloud Pak for Watson AIOps to detect incidents and achieve proactive incident resolution stemming from customers’ network environments. This helps users in the midst of an IT incident go from finger-pointing in the war-room to clearly and quickly locating failures for prompt resolution. Finally, Cloud Pak for Watson AIOps will feature new improvements to Infrastructure Automation, and it will now provide support for Red Hat Virtualization 4.4 and the System Z (in addition to the integration with Turbonomic), to ensure clients leverage existing deployments and increase ROI with proactive AIOps capabilities.

New features

Integration with IBM SevOne NPM

Cloud Pak for Watson AIOps v3.4 and the new connector with IBM SevOne NPM allow companies to add an additional tool on their journey to AIOps. In the past few years, the focus of the shift from IT Operations to AIOps has been application-centric. Today, with the launch of v3.4 of Cloud Pak for Watson AIOps, this application-centric approach shifts to full-stack observability through integration with the dynamic network monitoring and alerting capabilities from IBM SevOne NPM.

Enterprise scaling updates

According to the IDC, 80% of organizations estimate that they have over 1,000 applications in their portfolio, which means that now, more than ever, scalability is an essential part of AIOps. IBM Cloud Pak for Watson AIOps v3.4 delivers on the promise to serve organizations with dozens or thousands of applications with improvements to a few of our key features:

  • New ways of viewing events/alerts, relevant information and associated runbooks that can be used with existing Netcool components (e.g., Netcool/OMNIbus, Netcool/Impact) deployed in production.
  • The ability to investigate multiple metrics at one time.
  • Improvements to change-risk model performance using data profiling, data selection and user feedback.
  • The ability to create custom event, topology and metric data collectors.
  • Improvements to scalability, upgradeability, backup and restore and geo-redundancy.

Overall, our solution generates trusted opinions, insights and actions that enable users to consolidate and prioritize their work. Users spend less time identifying issues and can now allocate more time to innovation.

Learn more

To learn more, visit our IBM Cloud Pak for Watson AIOps page, and learn all about our product features with our product tour.

More from Artificial intelligence

In preview now: IBM watsonx BI Assistant is your AI-powered business analyst and advisor

3 min read - The business intelligence (BI) software market is projected to surge to USD 27.9 billion by 2027, yet only 30% of employees use these tools for decision-making. This gap between investment and usage highlights a significant missed opportunity. The primary hurdle in adopting BI tools is their complexity. Traditional BI tools, while powerful, are often too complex and slow for effective decision-making. Business decision-makers need insights tailored to their specific business contexts, not complex dashboards that are difficult to navigate. Organizations…

Introducing the watsonx platform on Microsoft Azure

4 min read - Artificial intelligence (AI) is revolutionizing industries by enabling advanced analytics, automation, and personalized experiences. According to The business value of AI, from the IBM Institute of Business Value, AI adoption has more than doubled since 2017. Enterprises are taking an intentional design approach to hybrid cloud and AI to drive technology decisions and enable adoption of Generative AI. According to the McKinsey report,  The economic potential of generative AI: The next productivity frontier, generative AI is projected to add $2.6…

Democratizing Large Language Model development with InstructLab support in watsonx.ai

5 min read - There is no doubt that generative AI is changing the game for many industries around the world due to its ability to automate and enhance creative and analytical processes. According to McKinsey, generative AI has a potential to add $4 trillion to the global economy. With the advent of generative AI and, more specifically, Large Language Models (LLMs), driving tremendous opportunities and efficiencies, we’re finding that the path to success for organizations to effectively use and scale their generative AI…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters