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AIOps

The latest News and Information on AIOps, alerting in complex systems and related technologies.

UBS invests in BigPanda to help drive digital disruption and innovation in AIOps

UBS is one of the leaders in the financial sector and one of the early adopters that are levering AI to do things better, cheaper and faster to bring their IT Operations in line with their cloud migration and digital transformation strategy. BigPanda is thrilled to have UBS as a customer and an investor to drive real transformation.

AIOps Feature Scape: How you can Accelerate AIOps Data Integrations with Insane New Robotic Data Automation Fabric (RDAF)

This is the first Feature Byte in the AIOps series. The idea of the Feature Byte series is to talk about key operational tasks and processes in AIOps, and how CloudFabrix Data-Centric AIOps platform features help implement such tasks. Look for more such feature bytes over the next few weeks.

Fast track video series: Integrate ticketing and messaging tools with BigPanda

BigPanda’s Agnostic Integrations provides powerful bi-directional integration for enterprise ticketing, service desk and collaboration tools such as chat and incident response, so operators can easily share BigPanda incidents with other users in their ticketing and collaboration tools of choice. With BigPanda, teams can easily automate ticket creation as well as notifications and war room creation in chat tools.

How Modern Infrastructure is Impacting Application Availability at Scale

The complexity of modern information technology (IT) infrastructures has grown exponentially and changed the way software companies operate and deliver products and services. The days of a single application server and a simple delivery path are long gone. Today’s application development and delivery process can encompass multiple platforms, cloud vendors, code libraries and customer bases.

Why AIOps may be necessary for the future of engineering

Machine learning has crossed the chasm. In 2020, McKinsey found that out of 2,395 companies surveyed, 50% had an ongoing investment in machine learning. By 2030, machine learning is predicted to deliver around $13 trillion. Before long, a good understanding of machine learning (ML) will be a central requirement in any technical strategy. The question is — what role is artificial intelligence (AI) going to play in engineering?