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AI / Operations

Causal AI: A Solution to Limitations of Correlation-Based ML

Because causal AI draws directly from data in context, it can identify root causes quickly without having to rely on a time-consuming learning model.
Jul 6th, 2023 6:13am by
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Organizations are turning to machine learning and artificial intelligence (AI) to tame the data in their multicloud environments so they can eliminate downtime, improve user experiences and make data-driven business decisions. Often, such machine learning is built on the assumption that the future will look a lot like the past. But in environments that rely on container-based microservices and open source software, AI that relies on machine learning algorithms isn’t keeping pace.

As more organizations embed AI into their IT operations (AIOps) and harness data to drive business decision-making, many have recognized the fundamental limitation of approaches built on correlation-based machine learning. Not only do machine learning models often take too long to build, they can’t adapt to the rate of change when services constantly scale up and down as business needs change.

It’s critical for businesses to match the best form of AI to the environment in which it operates and the tasks it needs to accomplish. Using the wrong approach to AI in multicloud environments can create a false sense of security by relying too heavily on correlation-based ML that can’t keep up. The consequences of that can be costly.

For this reason, causal AI has emerged in AIOps as an exciting alternative to traditional machine learning.

What Is Causal AI?

Causal AI is a form of artificial intelligence that uses cause and effect to inform decision-making. Because causal AI draws directly from data in context, it can identify root causes immediately without having to rely on a time-consuming learning model.

To understand the complexity and changeability of today’s enterprise environments is to appreciate the importance of causal AI. Applications and servers continuously churn data across logs, metrics and traces. When an error occurs, traditional machine learning algorithms parse through this data, suggesting potential sources of the problem based on what’s gone wrong in the past.

This approach works in a largely static environment dominated by monolithic applications, but it quickly breaks down in the dynamism of today’s hybrid, multicloud environments. Causal AI, on the other hand, continuously evaluates dynamic system data in real time, including the relationships among microservices and cloud-based infrastructure. With this real-time and comprehensive knowledge, causal AI can determine the exact issue with pinpoint precision, down to the code level. With this level of real-time accuracy, DevOps teams can automate responses, which leads to much faster time to resolution.

That level of specificity is not possible in a traditional machine learning application and is an approach better suited to modern IT operations and the resource constraints facing today’s organizations. For example, while traditional machine learning alerts teams of errors, humans must still investigate to verify the finding and track down what caused those errors in the first place.

Tackling Scale and Complexity of Modern IT Environments

Building a machine learning model that can deliver actionable insights doesn’t happen overnight. The model-training process can be extensive and requires spending significant time tuning and filtering out false positives.

In addition, the rapid pace of today’s complex distributed applications further complicates the process. Every change to the application environment requires the ML model to relearn how the environment works.

For example, in modern IT environments, the inability to handle novel situations can be a significant liability. Every new anomaly can trigger dozens of alerts, forcing DevOps teams to manually find the root causes of the issues, which can be time and resource intensive. For teams dealing with a proliferation of alerts, the ability to prioritize events is critical.

In contrast, consider the fault tree analysis approach taken by causal AI. Let’s imagine an application is experiencing a slowdown from receiving search requests. A fault analysis investigates the problem by first looking at the starting node of the tree (the application) before digging into the applications’ dependencies, which can include third-party calls and microservices-based applications. This process continues down the tree to the infrastructure level, tracing anomalies until the system identifies the root cause of the problem. The process also determines the severity and business impact of problems, for example, how much potential revenue or number of users are affected so that problems can be prioritized.

Causal AI builds dependencies in a bottom-up way, analyzing data for cause and effect while taking into account existing domain and expert knowledge. This approach eliminates the need for the development of ML models that are overly correlation-dependent, as both recognized patterns and novel situations easily “reveal their secrets” to observers. Because of this, causal AI is far more accurate, faster and more efficient than traditional correlation-based machine learning approaches to AIOps.

Causal AI Moves Beyond Statistical Convergence 

Machine learning assumes that its training data closely resembles actual real-world data. While this is an incredibly powerful approach for many use cases, it’s less effective in dynamic situations where there’s a complex web of potential causes. In a blog post, Leinar Ramos, a senior director of advisory at Gartner writes, “for correlation-based predictions to remain valid, the process that generated the data needs to remain the same.”

Causal AI, on the other hand, goes beneath surface-level correlation between variables, giving researchers a deeper and virtually real-time understanding of the true cause-and-effect relationships in the data. Instead of guesswork, it gives you precise answers.

For DevOps leads, this difference matters. The complexity of multicloud environments has put a premium on having full visibility into the entire technology stack, including all components and dependencies. When combined with causal AI, the granularity of this real-time topology allows organizations to rapidly trace problems to their root causes.

In addition, this streamlined approach to causation enables teams to automate responses to found problems. When teams can rely on the root-cause analysis and they have access to the full traces, teams can better understand how to reconfigure and avoid similar disruptions in the future. Through this shift-left approach, causal AI can improve decision-making by estimating the impact of specific actions to achieve a desired outcome.

Better at Predictive and Prescriptive Analytics, Too

A final consideration is that machine learning predicts the future based on what was, not what could have been or what might be.

In contrast, one of the most significant benefits of causal AI is its ability to model and simulate futures that aren’t limited by what’s already known. Examples would be causal AI predicting service-level objective violations proactively while pinpointing root causes or predicting IT capacity demands such as disk space or database performance. Causal AI can also trigger remediation actions because of its ability to answer questions with a high degree of precision. This form of predictive and prescriptive analytics enables organizations to anticipate situations and proactively address issues before they become expensive problems.

Building a Strong Causal AI Foundation

This discussion about the various approaches to AI isn’t just an academic concern. It has a direct impact on the efficiency and, in turn, profitability of organizations. As a result, it’s critical that teams develop a deep, nuanced understanding of the landscape, including the relative benefits and limitations of the technologies available to manage their complex environments. The future of their businesses may rely on it.

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TNS owner Insight Partners is an investor in: Pragma.
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