Faster insights for IT management and operations.

IBM is pleased to announce the latest release of IBM Cloud Pak® for Watson AIOps — version 3.2 — available as of November 5, 2021.

What’s new with version 3.2?

IBM Cloud Pak for Watson AIOps’ version 3.2 release focuses on addressing the increasing demands our customers are facing and providing a new AIOps user experience. Instead of a scavenger hunt to find the right answer or that one data source, Cloud Pak for Watson AIOps v3.2 collects and collates relevant information into a single, smart experience that seamlessly integrates with ChatOps. Additionally, v3.2 showcases the enhancements to our AI that now allow users to get started faster, with lighter-weight deployments.

New features

ChatOps and web console consistency:

  • IBM Cloud Pak for Watson AIOps v3.2 brings forward a new user experience, helping users quickly turn data into insight and action by bringing together incident and performance data from across their applications.
  • Our new story and alert dashboard experience allow users to see the bigger picture and dive as deep as they need to resolve the issue, saving users time by consolidating necessary information into one experience.
  • When responding to IT incidents, IBM Cloud Pak for Watson AIOps v3.2 automatically surfaces recommended next best actions sourced from similar prior incidents to resolve and drive down mean-time-to-resolution (MTTR).
  • Version 3.2’s ChatOps experience helps users quickly investigate and triage incoming incidents and alerts with the appropriate teams and subject matter experts.

Log anomaly detection within 30 minutes:

  • IBM Cloud Pak for Watson AIOps v3.2 introduces a real-time statistical model of log anomalies with continuous, concurrent training for rapid and accurate log anomaly detection. Get started with AIOps faster by training on and detecting log anomalies with AI in just 30 minutes, instead of the weeks or months that other AIOps solutions typically require.
  • Using AI to detect log anomalies helps teams respond to incidents more effectively, instead of managing static thresholds or attempting to fine-tune rules-based systems for their log data.

This latest release builds upon enhancements delivered in IBM Cloud Pak for Watson AIOps 3.1 and 3.1.1 releases earlier this year. IBM is excited to bring forward this new AIOps user experience to our customers and to continue to bring simplicity and a strong focus around consumability to the AIOps market.

Learn more

To learn more, visit our IBM Cloud Pak for Watson AIOps page and check out our latest webinar: Learn the value of Cloud Pak for Watson AIOps 3.2 release. You can also take a look at our IBM Cloud Pak for Watson AIOps documentation here.

More from Cloud

IBM Tech Now: April 8, 2024

< 1 min read - ​Welcome IBM Tech Now, our video web series featuring the latest and greatest news and announcements in the world of technology. Make sure you subscribe to our YouTube channel to be notified every time a new IBM Tech Now video is published. IBM Tech Now: Episode 96 On this episode, we're covering the following topics: IBM Cloud Logs A collaboration with IBM watsonx.ai and Anaconda IBM offerings in the G2 Spring Reports Stay plugged in You can check out the…

The advantages and disadvantages of private cloud 

6 min read - The popularity of private cloud is growing, primarily driven by the need for greater data security. Across industries like education, retail and government, organizations are choosing private cloud settings to conduct business use cases involving workloads with sensitive information and to comply with data privacy and compliance needs. In a report from Technavio (link resides outside ibm.com), the private cloud services market size is estimated to grow at a CAGR of 26.71% between 2023 and 2028, and it is forecast to increase by…

Optimize observability with IBM Cloud Logs to help improve infrastructure and app performance

5 min read - There is a dilemma facing infrastructure and app performance—as workloads generate an expanding amount of observability data, it puts increased pressure on collection tool abilities to process it all. The resulting data stress becomes expensive to manage and makes it harder to obtain actionable insights from the data itself, making it harder to have fast, effective, and cost-efficient performance management. A recent IDC study found that 57% of large enterprises are either collecting too much or too little observability data.…

IBM Newsletters

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