How can humans learn to trust self-healing machines? See how teams can build trust in machines through "truth and proof," evidence-driven AIOps tools. IT systems are only getting more complex, with greater pressures to solve issues faster and demonstrate value consistently. Issues within systems, which dev teams could once handle all on their own, sprout up too fast and too often for direct human intervention. Artificial intelligence for IT Operations (AIOps) tools exist today to deliver automated monitoring and solution development, "no humans required" -- significantly easing dev teams' many burdens.

Did you miss a session at the Data Summit? AIops -- the practice of applying AI to automate and improve IT operations -- has gained currency during the pandemic. As businesses embrace digital transformation strategies involving "multicloud," or the use of services from more than one cloud vendor, there's an increasing need to improve the observability and analytics around networking infrastructure and performance. In a 2022 Nutanix survey, organizations cited interoperability, security and data integration as the top challenges in managing mutlicloud setups. Spurred by the challenge, Kannan Kothandaraman and Nitin Kumar -- both networking industry veterans -- in 2019 launched Selector, an AIops platform for network, cloud and app delivery workflows.

AIOps (AI for IT operations) adoption is on the rise as organizations invest in AI to make their IT ops smarter, faster, and more secure. Those who have adopted AIOps view the technology as no longer a nice-to-have but a necessity in the post-pandemic, work-from-home era. IT leaders are tasked with managing third-party cloud applications from devices and remote workers scattered across numerous locations in this new era. The insights come from a recently published State of AIOps Study, conducted by ZK Research, sponsored by Masergy, a software-defined networking (SD-WAN) services company. In August 2021, ZK Research surveyed more than 500 IT decision-makers in the U.S. across seven industries. IT decision-makers believe AIOps offers their organization several business benefits, including improved productivity, cloud application performance, and security.

Elik co-founded BigPanda with a vision for enabling companies to pursue fully autonomous IT operations. For those of us in the tech space, you've likely heard of AIOps, or artificial intelligence for IT operations, which "involves using AI and ML technologies along with big data, data integration, and automation technologies to help make IT operations smarter and more predictive." The research firm Gartner recently defined two different high-level categories of AIOps: domain-centric and domain-agnostic. Domain-centric tools focus on homogenous, first-party data sets and introduce AI capabilities to solve specific use cases, such as network and application diagnostics. Domain-agnostic AIOps platforms combine diverse data sets and data types and synthesize them into insight or action.

"AIOps," which stands for "AI for IT operations," refers to the way data and information from a dev environment is managed by an IT team -- in this case, using AI. AIOps platforms leverage big data, machine learning, and analytics to enhance IT operations via monitoring, automation, and service desk functions with proactive and personal insights, enabling the use of multiple data sources and data collection methods. In theory, AIOps can provide faster resolutions to outages and other performance problems, in the process decreasing the costs associated with IT challenges. The benefits of AIOps are driving enterprise adoption. Eighty-seven percent of respondents to a recent OpsRamp survey agree that AIOps tools are improving their data-driven collaboration, and Gartner predicts that AIOps service usage will rise from 5% in 2018 to 30% in 2023.

To date, only a few open source projects label themselves as AIOps. Nonetheless, some open source platforms provide functionality that arguably qualifies as AIOps. For example, Kubernetes, which uses data analytics -- to a certain extent -- to automate workload orchestration, is partially an AIOps platform. Open source monitoring platforms, such as Nagios and Zabbix, also offer basic analytics functionality that you could consider to be AIOps, even if these tools don't define themselves as such. And a variety of open source programming language modules or frameworks, like PyTorch and TensorFlow, help implement AIOps functionality, even if they are not themselves complete AIOps platforms.

AIOps enables organizations to automate and accelerate IT tasks. Done properly, AIOps increases IT's flexibility and versatility, minimizes errors and faults, and frees IT staff to handle strategic tasks for the business. AIOps tools vary, but generally speaking, they ingest data from IT systems and analyze the information to discover patterns and anomalies related to how those systems perform. Watch the explainer video to understand how these tools gain insights, what machine learning does and the benefits and limitations users experience with these tools. Editor's note: Not an auditory learner, or want to add information from the video into your notes?

AIops (artificial intelligence for IT operations) is one of those cool buzzwords that is actually part of another buzzword: cloudops (cloud operations), which is a part of the mother of all buzzwords: cloud computing. The concept of AIops and the tool category of AIops are really the maturation of operational tools in general. Most of those in the traditional ops tools space, at least in the past few years, bolted an AI engine onto a tool and called it AIops. Some purpose-built AIops tool startups out there are leveraging AI from the jump. All are worth a look as you select AIops tools; however, there are no mainstream brands.

AIops (artificial intelligence for IT operations) is one of those cool buzzwords that is actually part of another buzzword: cloudops (cloud operations), which is a part of the mother of all buzzwords: cloud computing. The concept of AIops and the tool category of AIops are really the maturation of operational tools in general. Most of those in the traditional ops tools space, at least in the past few years, bolted an AI engine onto a tool and called it AIops. Some purpose-built AIops tool startups out there are leveraging AI from the jump. All are worth a look as you select AIops tools; however, there are no mainstream brands.

Artificial intelligence promises to enable machines or bots to take on the heavy-duty work of many parts of enterprises. Now, there are increasingly more initiatives, as well as vendor products, that will autonomously take on the heavy-duty work of information technology departments as well. The automation of IT functions has been evolving for decades, of course -- from job-scheduling systems in the 1990s to self-healing systems introduced more than a decade ago. These days, IT automation goes by many names -- such as autonomous systems, self-driving systems or bots. Lately, more of it is falling under the moniker of AIOps, joining the parade of xOps methodologies, promising to apply AI and machine learning to mechanize, standardize and automate the delivery of IT services.