AIOps - Artificial Intelligence in IT Operations - Part 2

 


In first part of the article, we looked at what is AIOps, how it works and what benefits you have using AIOps in your IT Operations. Now let us look at some AIOps strategies and go through some tools that have come up in the last 5+ years.



AI Ops Strategies


Don’t wait. Become familiar with AI and ML vocabulary and capabilities today, even if an AIOps project isn’t imminent. Priorities and capabilities change, so you may need it sooner than you expect.


Choose initial test cases wisely. Transformation initiatives benefit from starting small, capturing knowledge and iterating from there. Take the same approach to incorporating AIOps for success.


Develop and demonstrate your proficiency. Demystify AIOps for your colleagues and leadership by demonstrating simple techniques.


Experiment freely. Although AIOps platforms are often products of substantial cost and complexity, a great deal of open-source and low-cost ML software is available to enable you to evaluate AIOps


Standardize where possible, modernize where practical. Prepare your infrastructure to support an eventual AIOps implementation by adopting a consistent automation architecture


AI Ops Tools


Many companies have developed tools for implementing AI Ops over the last 5+ years since this concept has started picking. Here are the top 8:


BigPanda is a classic example of collecting data from many disparate services to feed its ML algorithms with data for correlation and problem identification. BigPanda captures and combines alerts with change and topology data from all your tools, then uses ML to spot problems and patterns that identify the root cause of performance issues or outages in real-time.

Moogsoft’s AIOps platform is another classic domain agnostic AIOps tool, providing noise reduction and causality analytics of the systems under observation. Service specific features are the workflow engine to provide an easy solution for creating custom logic to handle events, and the Situation Room which optimized communication and collaboration around a specific issue.

The Now Platform of ServiceNow includes all necessary components to deliver a full-fledged AIOps experience based on data collected from external data sources. 
AIOps with ServiceNow is fully integrated with all other services and departments supported by ServiceNow. Given that tight integration to other parts of the platform, operational intelligence and AIOps becomes part of the company’s basic habits.

Instana is a modern APM (Application Performance Management) platform, and therefore falls into the Domain Centric AIOps tools category. Instana uses its own Agent technology to automatically and continuously discover and monitor infrastructure and services, as well as collect Distributed Traces of all requests flowing through the system.

Dynatrace comes from a more traditional APM background but has rebuilt their monitoring platform to support modern application architectures. Dynatrace collects most data using its own agents making it a domain centric solution. Using its machine learning algorithms, Dynatrace focuses the user’s attention on issues and resources that really matter.

AppDynamic is another long-standing APM product which provides its own data collectors. Furthermore, integrations with external systems exist, and data retrieved is integrated into the correlation algorithms. With the Central Nervous System platform, AppDynamics (part of Cisco) provides a specific service platform purpose built to AIOps.

New Relic’s newly introduced New Relic AI capability provides features dedicated to AIOps. The New Relic One platform collects necessary information using its own agents (but recently announced that they will rely on OpenTelemetry agents in the future), stores, correlates and creates incidents. Integrations with external services and tools exist and are vast.

Broadcom provides CA Wily (formally a product of CA Technologies Inc). As part of Broadcom’s broader Bizops category, it provides all tools necessary to capture the information it needs to provide the insight and automation necessary to AIOps.

In summary, people can sometimes get scared by the notion of artificial intelligence thanks to science fiction films giving us the idea that the robots are taking over. But that’s simply not the case. Hopefully the above shows that artificial intelligence or machine learning simply create tools that can simplify and optimise the running of businesses. They are not invested in to take away the human aspect of a process, but rather work with people to create even better processes.

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