In this blog, I will answer the following general questions about AIOps:
- What is AIOps?
- What does a company need to utilize AIOps?
- How do I train AI?
- What is no-code AI?
- Why is this important?
What is AIOps?
Gartner’s definition of AIOps: AIOps combines big data and machine learning to automate IT operations (ITOps) processes, including event correlation, anomaly detection, and causality determination.
ITOps teams are beginning to explore how automation can improve business outcomes through scalable artificial intelligence (AI). Gartner found that 10 times more business leaders will rely on AIOps platforms for automated insights in the next three years. Without it, IT organizations can experience a lack of observability, become overwhelmed from manually managing data, and end up focusing on infrastructure rather than an application-centric approach. The ITOps teams of tomorrow will experience full visualization and observability of their IT environments with insights derived from various tools that focus on critical applications to support business performance.
What does a company need to be able to use AIOps?
To start, ITOps organizations can determine what the company needs in business performance. By applying AI, what does the company intend to do to improve outcomes? Next, companies will need to consider if the organization has historical and real-time data organized in a way that can begin to train models, and if so, is it understandable.
How do I train AI?
If a company wants to use AIOps they need to establish a baseline performance, which requires that they have an understanding of their historical data by measuring the performance of their steady state using real-time data. When something bad happens to a system, like an application outage, the company can more efficiently pinpoint the issue by using insights gathered from historical and real-time data. This insightful information can help find ways to triage events or outages as efficiently as possible.
What is no-code AI?
Companies don’t always have data scientists, data engineers, or data centric teams to help. What if we could train AI without the assistance of data scientists, data engineers, and data centric teams?
No-code AI simply means using no-code in automation training. A company can use its history and what it is doing today as a baseline to improve its tomorrow without developing code to train the AI.
Why is this important?
As an IT admin juggling multiple sources of data and resolving incidents manually, you need tools to help you resolve incidents faster.
Time is money. Battling aggressive timelines, ITOps teams need to identify solutions quickly and have those solutions work correctly when they’re set in motion. Let’s unpack how this is achieved with Cloud Pak for Watson AIOps.
So, what is the Cloud Pak for Watson AIOps?
Cloud Pak for Watson AIOps provides an application-centric data and intelligence platform powering automation for application, incident, cost, and security & risk management with trusted and explainable AI.
Figure 1: Bringing DevSecOps together with AI and automation
The goal of using AIOps is to focus on business outcomes. To do this, a company must leverage its data without creating gates of entry by requiring a dedicated data scientist, data engineer, or a data-centric team to get up and running. Cloud Pak for Watson AIOps provides a comprehensive understanding of business applications “baked in” to help provide insights and intelligence derived from not just operational data such as logs or events but enhanced organizational insights as well.
Learn more about Cloud Pak for Watson AIOps can evolve your ITOps organization. And, explore blogs, articles, tutorials, and code patterns on the Cloud Pak for Watson AIOps hub page on IBM Developer.