The latest News and Information on AIOps, alerting in complex systems and related technologies.
I am excited to announce today that BigPanda has secured $190 million in financing at a $1.2 billion valuation. This financing was led by Advent International and Insight Partners, together with our other existing investors. BigPanda is now officially a unicorn, and the clear leader in the rapidly growing AIOps market!
Time for a bit of a review! Join Jason as he looks back on some previous guests who have shared some opinions that range from the idiosyncratic to down right unpopular. Pulling from a handful of “Breaking Things” interviews, Jason covers death to VPNs, to the validity of “AI Ops,” check out the litany!
In our increasingly hyper-connected, data-dependent world, it can be difficult to keep track of where resources are, how to access them, and how to put data assets to work to run a more efficient and reliable enterprise. Traditional approaches to IT operations analytics are becoming outmoded as the sources and types of data grow more mobile, ephemeral, diverse and distributed.
Chestnuts roasting on an open fire. Jack Frost turning noses blue. Yuletide carols being sung by a choir, and folks predicting twenty twenty-two.
What comes first – observability or AIOps? Can you achieve observability without AIOps? Do you need AIOps if you already have an observability solution in place? These are all questions that any team considering AIOps will want to answer in order to determine the real-world value that AIOps tools stand to offer.
Complex enterprises have an integration infrastructure (i2) layer that connects technologies and applications across cloud, data center, virtualized systems, mainframe, edge computing, etc. The i2 layer includes a core middleware application (such as IBM MQ) along with many other "integration" technologies, such as MFT (managed file transfer), IoT, REST APIs, DataPower Gateway, and other messaging technologies (i.e., Kafka, TIBCO EMS, IBM ACE, IBM Integration Bus (IIB) and more).
Gartner predicted, by 2020 90% of Artificial Intelligence (AI) and Machine Learning (ML) would have been deployed in enterprises through “AIOps” – a combination of machine learning and operations. An AIOps approach has the potential to reduce costs and risks by automating routine IT Operations tasks while returning more control over decisions to the organization.
AIOps myths and how to avoid them Gartner coined the term AIOps in 2016 to refer to the combining of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.” In the five years since, AIOps has grown leaps and bounds — last year, AIOps was at the peak of the Gartner hype cycle.
2021 brought us widespread COVID vaccines, the Great Resignation, global supply chain disruption, inflation that went from transitory to persistent, accelerated digital transformation in the wake of the pandemic, an attempt at a return to normalcy—and the office—and plenty of uncertainty for the year ahead, thanks to the Delta and Omicron variants.
