What is AIOps?
AIOps is a term coined by Gartner. It was a new label for the tools that applied machine learning capabilities to the IT Operations space. All of us living in IT operations day in and day out know that we are inundated with data. The same kind of innovation people find that they can achieve by applying machine learning to other domains can also yield amazing transformative results by applying ML to data in the IT operations row. So AIOps is Gartner's term for that new category of tools and capabilities.
AIOps (Algorithmic IT Operations) is a platform solution that solves known IT issues and intelligently automates repetitive tasks. When it first came out, it was known as Algorithmic IT Operations. Now, in its latest version, it is known as Artificial Intelligence for IT Operations. You can also learn more about DataOps in this insight.
AIOps is solving the conflicts and use of the ticket routing algorithms has exponentially decreased the customer wait time and improves customer experience.Taken From Article, AIOps for Telecom Industry
Refers to Applying ML to Ops
- Monitoring and dealing with the alert data and metrics data.
- Service desk operations and ITSM operations are other big opportunities for applying ML.
- Automation is the third major domain or sub-domain of IT operations, and applying ML can achieve interesting results.
They all see those working synergistically, and they consider that AIOps aims to pull those together and use machine learning with the big data from all three to achieve better business outcomes. AIOps can reduce a company's cloud costs and improve cloud security compliance.
Why AIOps Matters?
Companies are leveraging AIOps for enhanced automation and faster execution of processes. AIOps can turn enterprises into:- Digital Transformation
- Smart DevOps and CloudOps Automation
- Faster Deployment
- Reduced MTTD and Faster MTTR
- Greater Visibility
- Real-Time Analysis
- Reduce Alert Noise
- Causal Analysis and Apply Analytics
- Data-Driven Recommendations
- Add values to Alert management, Automation, etc.
Monitor Pod Evictions to check cluster health, manage garbage collection, and Excessive load, Event Management, and Root Cause Analysis with AIOps. Taken From Article, AIOps Monitoring for Kubernetes and Serverless
Key Features of AIOps
Below listed are the top 9 Key features of AIOps
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Stored: AIOps is used to index and ingest historical data.
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Streaming: AIOps capture, normalise, and analyse real-time data.
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Logs: AIOps can capture and prepare text data from log files generated by software or hardware.
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Wire Data is used to packet data, including protocol and flow, and makes the information available for access and analysis.
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Document Text Data: AIOps can be used for data parsing, ingestion, and semantic and syntactical indexing documents.
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Anomaly Detection: AIOps uses the pattern to detect what constitutes normal system behaviour and then identify the departures.
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Automated Pattern Discovery and Detection: AIOps can detect mathematical or structural patterns in the data streams describing the connections to identify future incidents.
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Causal Analysis: AIOps uses automated pattern discovery to separate authentic causal relationships, with guide operator intervention to determine the root cause.
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Cloud: All the resources can be delivered in the cloud without installing any components on-premises.
Apply computation and algorithms efficiently and appropriately to expertise the machine and get desired outcomes. Taken From Article, AI for IT Infrastructure Management and Automation
Major challenges in AIOps adoption
The major 3 AIOps adoption challenges are highlighted below:
Poor Integration
The first biggest challenge is poor integration, so the phrase garbage in, garbage out works well. If we have bad data coming in, it is hard to produce any reasonable insights out of it, but it also makes sense if we get nothing in and nothing out, so in the end, we are not tying into the critical systems that we need data from. There is no way being on top of the analytics can make sense for us or get the expected value. So, the integration level is key. It would be best if you had lots of data. So, it's also important that when you get that data in, you normalize it and set a quality level where it is usable.
Misaligned Expectations
This came from Gartner. Now, as a consumer, you need to test your vendors. This is now done fairly easily; it is not like the old way, where we had to allocate thousands of pieces of hardware, get it wrapped and stacked, and then get a consultant out to install the software and all those pieces. Most of these components, as you know, have a cloud side to them, so you can actually be up in a few hours. The reality is that most of these platforms have some sort of SAS component, so you should be able to try this out and check it out within a day. If you cannot check it within a day, then you should try another product, and maybe it will be a little more complex than needed.
Misplaced Fear
The last part here is misplaced fear. The idea that may be a solution will eliminate the user's job or the tools that the user bought that made these kinds of promises which didn't work very well or just general reluctance to change these kinds of things. You always hit with specific types of projects. The job that one is interested in is AIOps solutions and more automation comes into the IP operation center. The operator's job will become cognitively demanding, so AIOps is a powerful tool that will be used to raise the value of the organization. So the idea is instead of doing simple manual labour, they are stepping up and having to do more troubleshooting and trying together multiple correlated and pattern insights, and it is a great thing for labour.
Combining the strength of AI in cyber security with the skills of security professionals from vulnerability checks to defense becomes very effective. Taken From Article, Artificial Intelligence in Cyber Security | The Advanced Guide
Who Uses AIOps?
Companies with extensive IT environments and working on multiple technologies are having difficulty expanding and issues while scaling. So, for them, AIOps can prove to be a life saviour. It can play a huge role in bringing success to the company. All organizations now want to scale rapidly and increase their growth, so they, in turn, create more demand for agility in IT. Through this insight, you can learn the applications and benefits of AI in banking.
DevOps Teams
All companies working on adopting a DevOps model or have already adopted it may struggle to maintain alignment between the roles involved. The direct combination and integration of Dev. and Ops into the overall AIOps model removes many of the problems at the interface. By confirming that Dev teams get the better understanding and knowledge of the state of the environment and Ops teams have full control over the visibility of how, when, and what changes and deployments are made by the developers that are put into production. This procedure ensures the success of the entire project and the achievement of agility and responsiveness.
Cloud Computing
As we move towards cloud computing, there are more challenges, especially when scaling the whole IT to the cloud. These models, including various forms of IT infrastructure, are very difficult to operate. AIOps removes most of the risk from operating a hybrid cloud platform.
Digital Transformation
There are various ways in which digital transformation initiatives can be defined, but the most important of them are speed and agility. This is a business requirement, but the IT must be operated at that speed per the business's requirement to achieve higher goals. AIOps help to remove most of the blockage that can later become a greater problem in IT from delivering greater and more successful high-quality digital transformation projects that are required.
What are the best open-source AIOps Tools?
AIOps uses artificial intelligence to simplify IT operations management and automate resolution, reducing the time it takes to resolve IT operations problems. Below are the most popular AIOps open-source tools.
Seldon Core
Seldon core turns machine learning models (Pytorch, Tensorflow, H2o, etc.) or language wrappers (Python, Java, etc.) into production-ready REST/GRPC microservices. Handles scaling to thousands of production machine learning models and provides advanced out-of-the-box machine learning capabilities.
Loglizer
Loglizer provides a toolkit that implements machine learning-based log analysis techniques for automated anomaly detection.
AIOpsTools
AIOpsTools is a toolkit for Python developers leveraging existing functionality to build AIOps applications. Aiopstools uses artificial intelligence to bring some ops scenes to life. You can easily import modules to achieve functionality.
Log Anomaly Detector
Log Anomaly Detector (LAD) is an open-source project code called "Project Scorpio." It allows you to connect to streaming sources and predict anomalous log lines. Internally, it uses unsupervised machine learning. LAD developers integrated a series of machine-learning models to achieve this result.
Log3C
Log3C is a popular framework for identifying problems in service systems using system logs. It lets you quickly and accurately identify critical issues using system logs and KPI metrics.
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