How IBM is Leveraging AI to Transform IT Operations?
As Information Technology (IT) complexity grows and the use of AI technologies expand, enterprises are looking to bring in the power of AI to transform how they develop, deploy and operate their IT. IBM is has recently launched a broad range of new AI-powered capabilities and services to help CIOs automate various aspects of IT development, infrastructure and operations, including:
• IBM Watson AIOps: leverages AI to reliably operate enterprise applications and automate the detection, diagnosis and response to IT anomalies in real time.
• Accelerator for Application Modernization with AI: a suite of tools within the Cloud Modernization Service designed to reduce the overall effort and costs associated with application modernization through advanced AI technology from IBM Research. This accelerator leverages continuous learning and interpretable AI models to adapt to a client’s preferred software engineering practices and stays up-to-date with the evolution of technology and platforms.
As is the case with much of IBM’s AI development, as per the company’s blog, significant portions of the technologies underlying Watson AIOps and the Accelerator were born out of IBM Research. This new offering and service — part of what the company is calling AI for IT — is the culmination of years of research and development at IBM Research into how AI can be used to transform the IT lifecycle.
CIOs and their application Site Reliability Engineering (SRE) teams are overwhelmed by the sheer number of tools for operations with data fragmentation across them and the complexity of issues, making IT operations a challenging domain. Watson AIOps reimagines IT operations with AI by enabling the prediction of problems and offering the potential of proactively fixing them. Breakthrough AI techniques developed by IBM Research enable Watson AIOps to discover new patterns in IT operations, remove noise, correlate problems across multiple data sources and make recommendations to fix them.
More specifically, the innovative tools and techniques in Watson AIOps correlate various multi-modal signals in IT operations by leveraging structured, semi-structured and unstructured data sources for building correlation models. For example, when an enterprise application experiences a problem, many monitoring tools start emitting a swarm of alerts, sometimes numbering in the thousands. Often, the root cause of these incidents lies in a different spot than the alerts. In fact, many times the swarm of these alerts distract SRE teams, wasting precious time during which the application continues to suffer operational instability or, even worse, failure.
SRE teams typically evaluate a multitude of data sources including metrics signals, application and system logs, the swarm of alert signals, and even past incident tickets. Many existing tools in IT operations can individually build models in each of these data silos to predict continuous values of the input signals or predict probabilistic class labels. But, they still treat the input spaces in isolation, resulting in hundreds of anomalies and, again, overwhelming the support teams. This is precisely where AI technology behind Watson AIOps steps in. It correlates among the diverse data sources to localize the real root cause, create an explainable diagnosis and recommend the best course of action.
To do the above correlation, the algorithms need to 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. 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.
