AIOps: Building the Next Generation of Intelligent Infrastructure

As engineers, we are constantly bombarded with complex machine data. In order to better monitor and troubleshoot our environments, we must analyze and gain an accurate understanding of this data to best evaluate how our systems are performing and combat any issues that may occur. Yet, sifting through this data while managing the added intricacies of serverless architecture, microservices, containers, and other technologies make our jobs increasingly difficult to navigate. 

Hence, the development of AIOps. Originally coined by Gartner, AIOps is the growing practice of combining Big Data and machine learning algorithms to work through the complexities of IT environments.

AI is a serious game-changer. For years, we have advocated the use of AI to help IT professionals with anomaly detection, monitoring, and automation and we’re not alone in this assessment. In fact, Gartner predicts that 40% of enterprises will use AIOps for monitoring, automation, and more by 2022.

Where does AI fit into IT Operations?

In the realm of IT operations/DevOps, the application and adoption of AI will drive innovation, while reducing human error and menial tasks. AI algorithms help to cut the clutter, detect anomalies, correctly identify issues in a system, and automate various processes.

Because of its tremendous potential, a growing number of machine learning platforms are being integrated into IT departments across the globe. As a result, organizations should envision a new operational paradigm which relies upon more and more machines to help engineers analyze their data in order to attain full visibility into their increasingly complex IT environments.

SaaS Comes of Age

The explosive growth of SaaS is a significant factor contributing to the need for AIOps. The evolution and growth of SaaS has enabled teams to increase product scalability and ease of use thereby enabling incredible technological innovation. That’s why SaaS adoption has increased tremendously in 2018, producing about $20 billion in revenue per quarter and increasing by 32% per year.

As a result of the development of SaaS, SaaS providers have access to data across different customers and use-cases, enabling many layers of innovation. But with the growth of its offerings comes the need to correlate and contextualize data. This is where AI is a huge benefit. AI can simplify the process of data contextualization across customer accounts and diverse data-sets. This gives SaaS providers the opportunity to create a network effect across their customer base.

The Game-Changing Power of AI-Driven Log Analysis

Another prime use case for AIOps is log analysis and automated root cause analysis. While Log Analysis has existed for years, in the past, it was often thought of as a tedious, cumbersome, and time-consuming process. AI and Machine learning help to reduce this complex and labor-intensive task, making it a more powerful tool for optimizing product functionality and reducing errors.

As businesses grow and their environments become increasingly complex due cloud adoption, serverless architecture, containers, and microservices, the need for a top-notch log analysis platform becomes even greater. As log analysis platforms work to gather and centralize log data, the billions of messages collected must be sifted through in order to discover their meaning. AI technology can be used for exactly this purpose.

For example,’s AI, Cognitive Insights and Application Insights, use machine learning algorithms and crowdsourced community knowledge to isolate and extract important insights from log data and exceptions so users can make better business decisions.

Securing your Infrastructure with AI

Just as AI-powered logging has proven itself to be a transformational asset for IT Operations, same goes for security. More and more, we are finding that DevOps personnel are responsible for at least a portion of their organization’s security operations. But, finding potential malicious activities amongst terabytes of data is next to impossible.

With machine learning, programmers can teach machines to understand which pieces of data indicate malicious activities. As a result, organizations are able to identify suspicious activities and stop potential hackers in their tracks preventing issues such as DDoS, malware, phishing, and more.

The Impact of AIOps

AIOps can truly change the face of IT and operations. By applying the technology in a variety of areas from log analysis, to SaaS management,  automation, security, and more–we can save time, energy, and money all while increasing accuracy. This combination of machine learning and Big Data improves a broad range of IT tasks including performance monitoring, event correlation, IT service management, and automation by powering data analytics both at the initial point of ingestion and later on as it is stored.

Because of its promise to improve the lives of IT Operations professionals as well as the products produced by them, we are working endlessly to integrate more AI and machine learning technology into, for smarter, faster, and more powerful monitoring, troubleshooting, and security.

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