An overview of IBM Cloud Pak® for Watson AIOps Version 3.1 and its new capabilities delivering automation in IT Operations.
Your time is meant to be kept as just that — your time. Avoiding application outages and automating the response when they do occur is the best way for you, as an ITOps organization or Site Reliability Engineering (SRE) team, to keep your weekend plans.
We are very excited to share an overview of the release of IBM Cloud Pak® for Watson AIOps Version 3.1 and its new capabilities delivering automation in IT Operations. This version was made generally available as of Friday, April 30, 2021.
Built on top of a common automation foundation, IBM Cloud Pak for Watson AIOps is designed to deliver on the promise of AI-powered Automation with applied artificial intelligence (AI) models for IT operations and management differentiated with application-centric insights and actions across DevSecOps processes. With that background, let us provide you with a sneak peek of the capabilities available to you today.
Common automation foundation
The IBM Cloud Pak for Watson AIOps is built on a common foundation of AI and automation that delivers a user-friendly, secured and consistent experience across IBM Cloud Paks®. This automation foundation provides a standard dashboard with a customizable, card-based layout of key data, fine-grained Role-Based Access Control (RBAC) on menu items and information displayed and a set of guided tours and tutorials to make getting started easy:
The automation foundation also acts as the common platform providing key capabilities like robotic process automation (RPA), process and task mining and natural language processing, fueling new innovations in our roadmap.
Accelerating the AI journey with a modern UX
A focus of this release is the user experience (UX). Installing, setting up, learning and operating needs to be as easy as possible. We’ve worked hard to tackle some of the common pain points our customers have told us about.
Installation: The single-click, operator-based install is designed so that customers can set up all components of the Cloud Pak in less than 60 minutes. This can help simplify and accelerate time to value for customers while providing extensibility through simple configuration.
Data ingestion: Data is the life blood of AI, but configuring data feeds can be complex. We are using this offering to help reduce that burden on our customers and deliver value faster. Our new guided user experience makes it easy for our customers to set up integrations to a wide variety of systems. IBM Cloud Pak for Watson AIOps helps boost integrations to large and commonly found ecosystem solutions, including log management, event management and service management tools within a customer’s IT environment:
Resource: A Live Demo
AI Hub: A key tenet of this release has been designed to simplify the UX around AI lifecycle management. The modern experience breaks the complex task of AI model training into a simpler, milestone-based experience, making it easy for a user to select and filter data, train the models and view the progress and its output. It is designed so that our customers will not need devoted data scientists’ resources to support data ingestion into the IBM Cloud Pak for Watson AIOps. Through this new experience, users can now train the built-in AI models of change risk, event grouping, log anomaly detection and similar incidents with their data with a few clicks:
Resource: Learn more about the models that make up AI Hub
Application Hub: What ultimately matters most to end-users is the health of their applications, not the infrastructure in and of itself. A failed disk is only as important as the applications it takes down. That is why we centered our management experience around applications. Application-centric insights and automation are designed to differentiate us from other vendors in the market; we don’t narrowly focus on either infrastructure or incident management. The built-in application template model dynamically aggregates and groups tagged resources, enabling rapid visualization of different application components:
The UX brings together different types of data and builds out an application perspective that delivers deeper insights from the AI models, engineered to deliver a high level of availability and reliability of the business application.
ChatOps: Our approach to ChatOps is focused on integrating with existing social collaboration tools and does not force users to learn proprietary tools. In addition to Slack, we are expanding our ChatOps user experience to be delivered through collaboration tools:
Resource: Live Demo with Teams and SNOW Integrations
We launched Watson AIOps last year and also purchased Observability with Instana. We are now rounding out our strategy with Application Resource Management (ARM) with Turbonomic. Last week, IBM announced plans to acquire Turbonomic, an ARM and Network Performance Management (NPM) software provider (and IBM partner) based in Boston, Massachusetts.
With Turbonomic, our AIOps capabilities can provide insights covering the entire IT stack. Managing application performance across hybrid and multicloud environments is a challenge for most IT organizations. ARM capabilities can help increase visibility, insights and automated actions to optimize resources such as containers, VMs, servers, storage, networks and databases, providing assured performance and minimizing costs for the organization. Get more information on this newly announced agreement.
Bringing it together
Our AI-driven insights combined with our new, modern user experience is designed so that customers can reduce Mean Time to Resolution (MTTR) when an incident occurs while also avoiding incidents in the first place, through our Change Risk Advisor.
Application Impact Avoidance: The trained AI models establish a baseline and automatically detect anomalies across structured and unstructured data. This reduces noise and brings related entities across different datasets together into a complete “story” ahead of a potential incident or application impact. A much smaller set of high-impact “stories” — versus a flood of events and incidents — removes tool fatigue that users experience today while providing deep insights with explainability. It helps users visually understand the blast radius, thus addressing any future downstream impact. The Next Best Action (NBA) recommendation from past incidents and the built-in Run Book Automation (RBA) capabilities can help accelerate the time to remediation of an incident. Furthermore, the fault localization capability can allow users to isolate and find the root cause of the problem faster.
Change Risk Advisor: Change is one of the primary contributors to outages. In our experience, the current methods of assessing the risks associated with a change are largely manual and a skill that is limited to a few within the IT teams. The manual techniques followed to assess risks are outdated and often result in either false positives or miss out on preventing an outage from occurring:
In the world of CI/CD of changes, these manual steps are not scalable and can slow down the business. IBM Cloud Pak for Watson AIOps delivers a set of AI technologies to quantify the risk of an IT change based on the past historical incident and change datasets. It not only scores the risk associated with a change, but also recommends possible actions through insights and explainability backed by historical evidence. The integrations with Service Management tools enable these insights to be delivered within those tools. Thus, it helps to proactively avoid potential outages caused by a change and can provide application reliability.
Resource: Deep Dive on Change Risk Management
Built on our common Automation Foundation, this release of the IBM Cloud Pak for Watson AIOps is designed to ease the path to adopting advanced AI for IT Operations. This approach can help lower operational costs and increase customer satisfaction by avoiding application outages and speeding time to resolution.
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