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Machine learning has crossed the chasm. In 2020, McKinsey found that of the 2,395 companies surveyed, 50% had an ongoing investment in machine learning. By 2030, machine learning is expected to generate about $13 trillion. Before long, a good understanding of Machine Learning (ML) will be a prerequisite in any technical strategy.
The question is – what role will artificial intelligence (AI) play in engineering? How will the future of code building and deployment be affected by the advent of ML? Here, we will discuss why machine learning has become central to the ongoing development of software engineering.
The increasing rate of change in software development
Companies are accelerating the rate of change. The programs were published once a year or every two years. Now, two-thirds of surveyed companies publish at least once a month, with 26% of companies posting multiple times a day. This increasing rate of change shows that the industry is accelerating the rate of change to keep pace with demand.
If we follow this trend, almost all companies are expected to publish changes several times a day if they want to keep pace with the changing demands of the modern software market. Scaling this rate of change is difficult. As we accelerate faster, we will need to find new ways to improve the ways we work, tackle the unknown and advance software engineering into the future.
Enter Machine Learning and AIops
The software engineering community understands the operational burden of running a complex microservices architecture. Engineers typically spend 23% of their time on operational challenges. How can AIops lower that number and allow time for engineers to get back into programming?
Use AIops to alert you by detecting anomalies
The common challenge within organizations is disclosure anomaly. Anomalies are those that do not fit into the rest of the data set. The challenge is simple: how do you define deviations? Some datasets come with comprehensive and diverse data, while others are very similar. Classification and detection of sudden change in this data becomes a complex statistical problem.
Detecting anomalies through machine learning
Anomaly detection is a machine learning technique that uses the pattern recognition powers of an AI-based algorithm to find outliers in your data. This is incredibly powerful for operational challenges where human operators typically need to filter noise to find actionable insights buried in the data.
These ideas are compelling because your AI approach to alerting can raise problems you’ve never seen before. With a traditional alert, you’ll usually have to anticipate what incidents you think will happen and create rules for your alerts. This can be called your own well-known or your unknown unknown. Accidents you are aware of or blind spots in surveillance that you cover just in case. But what about files unknown?
This is where your machine learning algorithms come in. AIops-based alerts can act as a safety net around your traditional alert so that if sudden anomalies occur in your logs, metrics, or traces, you can work with confidence that you’ll know. This means less time identifying incredibly accurate alerts and more time creating and deploying features that will differentiate your company in the marketplace.
AIops can be your safety net
Instead of defining countless traditional alerts around every possible outcome and spending a lot of time creating, maintaining, modifying, and tuning those alerts, you can define some of your primary alerts and use your AIops approach to capture the rest.
As we evolve into modern software engineering, engineers’ time has become a scarce resource. AIops has the potential to cut down on increasing software OPEX and save time for software engineers to innovate, develop, and grow in the new era of coding.
Ariel Sarraf is the CEO of Coralogix.
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