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With digital transformation becoming an organizational priority, the push to roll out robust and reliable software applications faster is increasing.

DevOps has been an enabler of the same and has given organizations the velocity to push out robust, reliable, and secure software applications to meet user needs at speed. It has also given them the capacity to increase enterprise agility, especially as we move further into the application economy. 

But as the world is revolving more around apps, IT infrastructure teams have a new beast to tame – the beast called ‘complexity’.

The challenges of IT infrastructure teams

•  Multiple Technologies: IT infrastructure teams are now fighting a raging battle against the rising complexities of the applications and technology stacks. This complexity is often encountered when teams expand and scale and start using multiple technologies to meet their growing needs. 

•  Connectors and Custom Apps: However, too many technologies complicate the overall technology stack. Since the technology landscape exists in a state of dynamic equilibrium, new technologies, and technology stacks are often unable to adapt to this state. To solve this problem, most organizations work towards building or buying connectors/custom apps to connect these disparate systems. However, instead of solving the problem, it introduces more complexity.

•  Monitoring Tools: The number of data and monitoring tools to manage these large and complicated technology stacks is also increasing. While the objective of these tools is to provide deep and clear insights on how to increase and improve the performance of the infrastructure environment, too many cooks spoil the broth here. 

•  Alert Noise: More data and monitoring tools might mean more alerts, but how many of those alerts are the right ones? Infrastructure teams are reeling under the pressure of too many false alerts and noise. And, false alerts and noise are counterproductive no matter which way you shake it. Investing time and resources to track down an alert that is of no consequence is a waste (heard of alert noise?). Ignoring them means tempting Murphy’s law to kick in – systems go down, and performance gets impacted.

•  Siloed Working: And then there are the challenges that arise from IT and operations teams existing in silos, which also add to the complexity of IT infrastructure challenges. All of these issues contribute to the IT infrastructure nightmare and make it hard for these teams to identify and resolve issues at speed.

AI Ops comes to the rescue 

Today’s dynamic IT environment demands an evolution of IT infrastructure to manage constantly changing IT environments. The older, rules-based systems fall short to address the demands of today’s IT infrastructure since they depend on predetermined, static representations of a mostly homogeneous and self-contained IT environment. 

To battle most infrastructure challenges and also accommodate the growing data needs, organizations have moved (or moving) towards the cloud. The cloud simplifies the development, deployment, and monitoring of scalable applications and helps organizations capably manage IT environments that are distributed, dynamic, and component oriented.

By facilitating real-time monitoring, predictive analysis, and root cause analysis, AI Ops helps organizations in optimizing their cloud investments.

AI Ops (Artificial Intelligence for IT operations) helps DevOps and IT Ops teams work smarter, faster, and better by helping these teams detect digital-service issues faster and resolve them before they have any impact on productivity, business continuity, or customers.

It also gives Ops teams the capability to tame the complexity dragon and manage the volume of data. Maintaining uptime, ensuring continuous service assurance and outage prevention become easier and manageable.    

But how does AI Ops do this? 

AI Ops is a multi-layered technology platform that helps in automation and enhancement of IT operations using analytics and machine learning. AIOps platforms use big data and a host of other data from the several IT operations tools and devices to identify and solve issues in real-time while providing historical analysis as well.


The Benefits of AI Ops

AI Ops helps in covering all the operational challenges facing IT infrastructure and drive better customer experiences. 

Some of the benefits of AI Ops are:

•  Get clearer insights: Since AI Ops uses big data and machine learning, it needs IT infrastructure to move away siloed IT data to aggregate observational data (data in job logs, monitoring systems, etc.) and engagement data inside a big data platform. 

AIOps employs advanced analytics and machine learning strategies against the combined data to get automation-driven insights. These insights then yield continuous improvements and proactive fixes and help organizations proactively improve system availability, drive better performance, identify performance inhibitors and reduce costs

•  Eliminate irrelevant alerts noise: AI Ops reduces irrelevant event noise and alerts and removes the deluge of event volumes. It ingests data from several sources and technologies and aggregates different data types in a single repository. It gives IT teams the capacity to employ policy-based rules that can be used to suppress unnecessary alerts and events.

Machine learning algorithms effectively reduce noise by pattern identification based on established baselines. AI Ops also enables predictive alerting to detect anomalies and potential issues, prioritize them for triage and diagnosis, and ensure remediation before the issue has a business impact.

•  Facilitate smooth collaboration between teams: It facilitates better collaboration by enabling workflow activities between IT groups as well as between IT and other associated business units. The customized dashboards and reports provide clear insights into task allocations and requirements and provide the clarity needed to drive collaboration in today’s work environment.

•  Enable proactive performance monitoring: Since AIOps eliminates the data silos, it helps organizations enable proactive performance monitoring by taking in the totality of application environment data. This approach helps AI Ops platforms connect performance insights to business outcomes, set performance benchmarks, and raise timely alerts so that any performance-related incident is eliminated before it occurs.

•  Decrease MMTR: Most organizations are on the warpath to increase their capacity to reduce the Mean Time to Resolution (MTTR). AI Ops helps organizations develop their predictive capabilities to identify outages and performance issues before they occur and help IT teams decrease MMTR and prevent emerging issues.

•  Automate root cause analysis: AI Ops automates root cause analysis by applying machine learning algorithms. Doing this helps to map complex architectures with casual relations and go deep into the root of the problem much faster than manual analysis. It then detects how issues impact business service and co-relates metrics, events, anomalies, and data logs to identify issues. It employs analytics to find the root cause and also tracks event patterns of applications to find the same.

•  Strengthens SRE (Site Reliability Engineering): According to a Gartner report, DevOps teams must use site reliability engineering to maximize customer value. Since the need for improved reliability has become mission-critical, organizations have to look at AI Ops to make use of intelligent recommendations and automated remediations and create more resilient production environments.

AI Ops creates a continuous feedback loop between development, operations, and business teams and makes sure all are working towards the same goal and are on the same page. It creates a single source of truth that enhances collaboration and provides AI-driven intelligence that covers the entire development cycle. These capabilities invaluably complement the site reliability engineers' skills and help in rolling out stable and reliable products.

AI Ops is beginning to assume the role of an important character in the DevOps story. With AI Ops, DevOps teams can now expand the automation footprint and make processes faster and easier to improve outcomes, proactively identify hurdles, and eliminate wasteful efforts. The shift to AIOps has long been coming, mainly because traditional IT management techniques will soon be unable to cope with the push towards digital business transformation.

It’s time to get on the AIOps bandwagon even if you only want to enable innovation and manage the volume, variety, and velocity of business data and stay ahead of the curve.

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