News Roundup, June 17: What’s Happening in AIOps, ITOps, and IT Monitoring

Speaking of creating projects that scale, it’s time to read up on the latest about AIOps, ITOps, and IT infrastructure monitoring.

1.) The push is on to build an autonomous enterprise.

This article by Business Telegraph analyzes how organizations are moving towards an autonomous enterprise.

Enterprises are in the early phases of a revolution whose mission is to make more kinds of business systems, equipment, and processes perform with less human intervention. This autonomous revolution is rapidly moving from one-off experiments to a collective effort to build digital fabrics that can keep up with the rapid pace of change in supply chains, geopolitics, and the environment. The promise? An increase in efficiency, scalability, and profitability at a level previously unknown.

Progress is being driven by a convergence of cloud, data, and IoT infrastructures. As these technologies become more connected, they are stitching pockets of autonomous systems into a more capable and coherent whole.

The result is that physical spaces, business processes, and IT are being transformed across industries and across enterprise departments:

• Physical spaces;
• Business processes; and
• IT operations.

Advances in autonomous systems will need to be balanced with tools—such as digital twins and the metaverse — that provide a window into how automation is playing out. Improved context will help humans more quickly detect emerging problems and provide better guardrails for autonomous systems that make decisions at scale.

2.) There’s proof that complexity leads to cloud outages.

This article by Infoworld breaks down how complexity has the potential to lead to cloud outages.

Although we get different messages from cloud computing providers, we now have data that suggests public cloud outages are getting worse. One in five organizations reported a “serious” or “severe” outage that resulted in significant financial losses, reputational damage, compliance breaches, or, in some severe cases, loss of life. The report concludes that there has been a slight upward trend in the prevalence of major outages in the past three years.

Complexity is not a new challenge for IT. However, we recently created much more complexity through quick digital transformations and the wild rush to cloud and multi-cloud in response to the pandemic. These factors resulted in a new, high headcount in the types of systems that support businesses. Most enterprises reported that they once supported about 500 cloud services for the entire enterprise and now support about 3,000 services over a multi-cloud deployment.

These numbers indicate that the technology doesn’t cause the outages; it’s how the technology is used and the amount of technology in use. As the report states, 40% of organizations have suffered a major outage caused by human error. Of these incidents, 85% have a root cause of staff failing to follow procedures or flaws in the processes and procedures themselves.

The result is a complex and highly heterogenous multi-cloud deployment that requires staff with specialized skills to effectively operate and limit the number of outages. Ironically, most IT organizations can’t get approval for an increased ops budget because cloud computing promised to make operations less expensive.

3.) Let’s learn about the differences between MLOps and AIOps.

This article by Hackernoon elaborates on the differences between MLOps and AIOps.

MLOps and AIOps are two similar-sounding terms that are used to refer to vastly different disciplines within the industry today. Due to their similarities, it’s not surprising that non-practitioners today run into comprehensibility barriers regarding MLOps and AIOps technology, toolsets, and practices. DevOps started to become mainstream around 2007 in response to a common organizational problem that affected product teams in their ability to ship software at a brisk pace.

The DevOps lifecycle has 6 phases:

• Plan
• Build
• Continuous integration and deployment
• Monitor
• Operate
• Continuous feedback

MLOps rose into prominence around 2015 with the promise of solving critical operational problems pertaining to the end-to-end delivery of machine learning pipelines, like the ones that DevOps had solved a decade earlier.

The MLOps lifecycle has 9 phases:

• Plan
• Create and gather data
• Model and verify
• Package and release
• Configure and monitor

4.) Here’s why AIOps is considered essential.

This article by Betanews explains why AIOps is more essential now than ever for businesses.

Development teams are being forced to ‘shift left’ under pressure from the business to move more work closer to the design and development phase, earlier in the process. The idea is to catch bugs earlier, before they turn into costly production outages, and should improve efficiency while minimizing risk within the software development cycle. Yet this demand puts even more pressure and responsibility on developers.

One thing that can make delivery of this business easier—and result in better outcomes—is to inject AI-powered tools into the codebase to catch the risks hidden within supply chain complexity and fast pace of work.

There are three reasons to consider using AI-driven development aids in your codebase:

• Enhance your understanding of your code;
• Increase your software quality; and
• Increase the security of your product.

Efficiency, effectiveness, and better developer experience is all possible, but only with the right tools to support developers in what is one of the most complex and demanding jobs—where failures from products in use by customers can make or break a business.

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