What is AIOps?
AIOps is a term coined by the Gartner. It was a new label for the tools that took machine learning capabilities and applied them to IT Operations space. All of us who are living in IT operations day in and day out, we know that we are inundated with data. The same kind of innovation the people are finding that they can achieve by applying Machine Learning to other domains can also yield really amazing transformative results by applying ML to data that in the IT operations row and so AIOps is the Gartner's term for that new category of tools and capabilities.
AIOps (Algorithmic IT Ops) is a platform solution that solvers known IT issues and intelligently automates repetitive tasks. When first came out with AIOps it was known to be as Algorithmic IT Operations. Now as the latest it is known as Artificial Intelligence for IT Ops. 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 is another big opportunity to apply ML.
- Automation is the third major domain or sub-domain of IT operations we can achieve interesting results by applying ML.
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
What are the Key Features of AIOps?
Below listed are the top 9 Key features of AIOps
- Stored: AIOps is used for indexing and ingestion of historical data.
- Streaming: AIOps is used for capture and normalization and analysis of real-time data.
- Logs: AIOps can be used to capture and prepare the text data from log files that are generated by the software or the hardware.
- Wire Data: It is used to packet data, including protocol and flow the information and made it available to access and analysis.
- Document Text Data: AIOps can be used for data parsing, ingestion and semantic and syntactical indexing of the document.
- Anomaly Detection: AIOps uses the pattern to detect what constitutes normal system behavior and then identify the departures.
- Automated Pattern Discovery and Detection: AIOps has the ability to detect the mathematical or the structural patterns which are in the data streams that describe the connections that are used to identify further future incidents.
- Causal Analysis: AIOps uses automated pattern discovery to separate authentic causal relationships with guide operator intervention for determining the root cause.
- Cloud: All the resources can be delivered in the cloud, without any need of on-premises installation of any of the components.
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
What are the major Challenges of AIOps adoption?
The major 3 AIOps adoption challenges are highlighted below:
The first biggest challenges are 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 for if there 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 possible way that on the top in the analytics can make sense for us or get the value we are expecting out of it. So the integration level is absolutely key. You need the data lots of it. So, it's also important that when you get that data in, you normalize the data and set a quality level where it is usable.
This actually came from Gartner. Now as a consumer it is important for you to actually to test your vendors. This is now done fairly easily, it is not like the old where we have to allocate thousands of worth of hardware and get it wrapped and stacked and then get a consultant out to install the software and all those pieces. In most of these components as you know has a cloud side of it, so you can actually be up in a few hours. The reality is most all these platforms have some sort of SAS components so you should be able to try this out and check it out within a day. If you are not able to check it within a day, then you should try another product and maybe it is a little more complex than needed.
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 actually interested in is AIOps solutions and more automation comes into the IP operation center. Really the operator's job will become cognitively demanding, so AIOps is a powerful tool that will be used and actually raise the value of the organization. So the idea is instead of doing simple manual labor they are actually stepping up and having to do more doing, more troubleshooting and trying together multiple correlated and patter insights and it actually a great thing for labor.
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 which have extensive IT environments, that are working on multiple technologies are having difficulty to expand and issues while scaling. So for them, AIOps can be proved to be a life savior. In fact, it can play a huge role in bringing success to the company. All the organization now wants to scale rapidly and increase their growth so they, in turn, creates more demand for agility in IT. Know the applications and benefits of AI in Banking in this insight.
DevOps TeamsAll those companies who are working on adopting a DevOps model or have already adopted may struggle in maintaining alignment between roles involved. The direct combination and integration of Dev. and Ops into the overall AIOps model takes away much of the problems that can occur at the interface. By confirming that Dev teams get the better understanding and knowledge of the state of the environment and Ops teams full control over the visibility how and when and what changes and deployments are made by the developers that are put into the production. This procedure ensures the success of the entire project and the achievement of agility and responsiveness.
Cloud ComputingAs we move towards cloud computing there are more challenges, especially when it comes to scaling the whole IT to the cloud. These models including various forms of the IT infrastructure are very difficult to operate. AIOps removes most of the risk from the operation of a hybrid cloud platform.
There are various ways in which digital transformation initiatives can be defined but the most important of them is more speed and agility. This is basically a business requirement, but the IT must need to be operated at that speed as per the requirement of the business so as per to achieve higher goals. AIOps helps to remove most of the blockage that can later become a greater problem in IT from delivering greater and 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 to reduce time to resolution of IT operations problems. Below are the most popular AIOps open source tools.
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 provides a toolkit that implements a suite of machine learning-based log analysis techniques for automated anomaly detection.
AIOpsTools is a toolkit for Python developers who want to leverage 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". You can connect to streaming sources and make predictions about anomalous log lines. Internally, it uses unsupervised machine learning. LAD developers integrated a series of machine-learning models to achieve this result.
Log3C is a popular framework for identifying problems in service systems using system logs. Identify critical system issues quickly and accurately using both system logs and system KPI metrics.
When we work with AIOps it does not replace the tools that are used for monitoring or management or orchestration. Instead, AIOps sits at the intersection of all these discrete domains, using and integrating information among all of them and hence providing the necessary output to safeguard a synchronized picture which is available from all the tool. All these tools are valuable in their own right, but it can become hard to use the right piece of information at the exact/right time, as long as they are disconnected. AIOps makes the use of machine learning, data science and other algorithms for analyzing the data and automate things.
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