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What the evolution of AIops solutions means for businesses


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Without exaggeration, digital transformation is moving at breakneck speed and judgment is that it will only move faster. Follow Gartner.

Driving this wild, fast ride is data, and this is why for many businesses, data – in various forms – is one of its most valuable assets. As businesses now have more data than ever before, effectively managing and leveraging that data has become a top concern. A key concern among them is the inadequacy of traditional data management frameworks to handle the increasing complexity of the digitally transitional business environment.

The priorities have changed: Customers are no longer satisfied with traditional immobile data centers and are now moving to multi-volume, on-demand, and high-capacity centers. Based on Forrester’s survey Among 1,039 international application development and delivery professionals, 60% of technology practitioners and decision makers are using multi-cloud – a number that is expected to grow to 81% in the next 12 months. But perhaps the most important takeaway from the survey is that “90% of multi-audio users who responded said it helped them achieve their business goals.”

Manage the complexities of a multi-cloud data center

Gartner also reported that enterprise multi-cloud deployments have become so ubiquitous that at least until 2023, “the 10 largest public cloud providers will command more than half of the total cloud market.” public cloud”.

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But that’s not where it ends – customers are also on the hunt for edge, private, or hybrid multi-cloud data centers that provide full visibility into the enterprise-wide technology stack and interoperability. inter-domain relationship of IT infrastructure components. Although logical, these functions come with great complexity.

Typically, layers on top of cross-domain configuration layers are typical for multi-cloud environments. However, as newer cloud capabilities go mainstream, new layers are required – thus further complicating an already complex system.

This is further complicated by the deployment of 5G networks and edge data centers to support the growing cloud-based demand of the post-pandemic global climate. Ushering in what many have called “a new wave” data centersThis reconstruction creates even greater complexity that puts enormous pressure on traditional operating models.

Change is necessary, but considering that the slightest change in one of the infrastructure, security, network, or application layers can lead to a large-scale butterfly effect, enterprise IT teams must Accept the fact that they cannot do it alone.

AIops as a solution to the complexity of multiple clouds

Andy Thurai, Vice President and Principal Analyst at Constellation Research Inc., also confirmed this. For him, the difficult nature of multi-cloud operations management has led to the increasing complexity of IT operations. His solution? AI for IT operations (AIops), an AI industry category created by the technology research firm Gartner in 2016.

Officially defined by Gartner as “a combination of big data and ML” [machine learning] in automating and improving IT operational processes”, AIops’ detection, monitoring, and analytics capabilities allow it to intelligently combine across a multitude of different components of data centers to provides a comprehensive transformation of its operations.

By 2030, the increase in data volumes and the resulting increase in cloud adoption will contribute to expected Global AIops market size 644.96 billion USD. This means that businesses that expect to meet the speed and scale requirements of growing customer expectations must resort to AIops. On the other hand, they run the risk of poverty data management and as a result, business performance declines.

This need creates a need for comprehensive and comprehensive operating models for implementing AIops – and that’s where Cloudfabrix come in.

AIops as a Composable Analytics Solution

Inspired to make it easy for businesses to adopt data-driven, AI-first, and automation strategies everywhere, Cloudfabrix today announced the availability of its new AIops operating model. It is equipped with personality-based meta-analysis, data and AI/ML visibility pipeline and troubleshooting process capabilities. The announcement comes after the recent release of what it described as “a world first robot data automation fabric (RDAF) technology unifies AIops, automation and visibility. “

Identified as the key to AI scaling, meta-analysis gives businesses the opportunity to organize their IT infrastructure by creating subcomponents that can be accessed and distributed to remote machines at will. Featured in Cloudfabrix’s new AIops operating model is the integration of aggregable analytics with aggregateable pipelines and dashboards.

Providing a 360-degree visualization of different data sources and types, Cloudfabrix’s aggregated dashboards feature field-configurable, personality-based dashboards with centralized visibility for Platform team and KPI dashboard for business development activities.

Shailesh Manjrekar, Vice President of AI and Marketing at Cloudfabrix, noted in a article announced in Forbes that the only way AIops can process all kinds of data to improve their quality and gather unique insights is through real-time observability pipelines. real. This stance is echoed in Cloudfabrix’s adoption of not only composable pipelines but also observable pipeline aggregators in its troubleshooting process.

In this compilation, possible malfunctions are simulated to monitor pipeline performance and understand possible causes and their solutions. Also included in the model’s troubleshooting process is the recommendation engine, which leverages behavior learned from the operations center and NLP Analysis to suggest clear corrective actions for priority alerts.

To comment on the scope, Cloudfabrix CEO Raju Datla said the launch of its composable analytics is “only focused on the BizDevOps personality and transforming the user experience.” and their trust in AI operations.”

He added that the launch is also “focused on automation, by seamlessly integrating AIops workflows in your operating model and building trust in data automation and pipelines.” observability through simulation of synthetic failures before launch in production.” Some of the active characters for which this model has been designed include cloudbizops, GitOps, finopssorry, DevSecOpsEnforcement, Sorry and services.

Founded in 2015, Cloudfabrix specializes in helping businesses build autonomous businesses with AI-powered IT solutions. While the California-based software company markets itself as a leading provider of data-centric AIops platforms, it’s not without competition – especially with rivals like IBM Watson AIops, Moogsoft, Splunk and others.

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