In this third edition of our series on AI-powered Operations (AIOps), we take a closer look at the technical components that form the foundation of a successful AIOps project. They are crucial in enabling companies to make the right decisions in the blink of an eye. Now, let’s take a closer look at each of these building blocks!

AIOps strategy

When it comes to AIOps, everything starts with finding a match between current data sources and desired alerting mechanisms. This strategic approach ensures that the beginning and end of your pipeline are well defined and you need to assess the quality of both before you can build an AIOps pipeline. Let’s look at some concrete questions you need to answer before integrating AI into your business-critical operations:

  • Data quality: Do you have the right data to trigger an alert? Is this data complete enough so that someone looking at it can draw a conclusion linked to the desired alert?
  • Actionable alerts: Are the alerts you want to produce actionable? When your user receives this alert, does it provide a more efficient indication of the work that needs to be done?
  • Real-time data: Is your system prepared to produce data in real-time? Are your processes organized to interface with data streaming solutions?

Once you have defined a clear goal for your AIOps pipeline and confirmed that your organization is ready for real-time AIOps capabilities, the next steps involve laying the pipeline between big data and actionable insights.

Edge devices as streaming data sources

Real-time data is the lifeblood of AIOps. It is therefore crucial to have devices that can generate this data. This section explores the practical setup required:

  • On-premises infrastructure: This involves a solid setup on-site to catch signals from important business processes. These signals are the core of real-time data insights, so a strong on-site system is crucial.
  • Cloud resources: At the same time, we take into account cloud resources needed to transmit data quickly. This is where messages packed with data find their way into real-time analytics. A well-organized cloud setup is vital for smooth integration.
  • Vital tools for communication: Message brokers like RabbitMQ and Apache Kafka play a pivotal role in facilitating smooth communication between devices and processing hubs, ensuring that critical business data moves swiftly and reliably.

Real-time data ingestion and processing

When it comes to AIOps, the processing of incoming data differs significantly from batch data ingestion. Organizations rely on AIOps for critical business operations and settle for nothing less than real-time insights. Moreover, the vast amounts of data produced by such crucial activities tend to be enormous. These challenges must be addressed in the first steps of the data pipeline, often in the form of data aggregation. Aggregating data is like combining puzzle pieces to see a bigger picture. In some cases, it helps understand trends and patterns that may not be immediately apparent from individual data points. Tools like Apache Spark, Flink, Beam and Hadoop make it possible to aggregate big data in real-time, providing valuable insights without causing significant processing delays.

AI-powered model

AI-powered models are at the core of an AIOps solution. When creating these models for specific business cases, there are several trade-offs to be made. One of the most influential decisions is whether to enhance an existing open-source model or create a custom model tailored to your needs.

  • Pre-trained models offer a swift starting point, like expert-designed templates, excelling in handling real-time data for quick, reliable insights.
  • Custom models cater to unique business needs, providing specificity and precision. Combining inference and decision models yields powerful results, guiding crucial business decisions and enhancing outcomes.

TensorFlow and PyTorch stand out as versatile platforms for developing and implementing machine learning models. They provide a robust foundation for working with both pre-trained and custom models. Their flexibility and extensive libraries make them indispensable tools in this process.

Solution deployment

Breaking the system down into manageable components is essential for smooth operation. Components can operate independently while working together to create a seamless solution. Docker is used to package these components along with their dependencies, ensuring consistent performance. Kubernetes manages container deployment, connects containers in a cluster network and handles resource allocation efficiently to ensure high availability and scalability. Setting up an API acts as a bridge to the outside world, enabling smooth communication with other systems and users and enabling real-time interaction, seamless integration and automated processes.

Business intelligence and alerting

This phase emphasizes user interaction with the solution through intuitive dashboards and applications that facilitate real-time data access and rapid responses. These tool present data clearly, allowing users to monitor system performance, key metrics and swiftly address issues. Additionally, we offer applications that connect users to the system’s intelligence, facilitating seamless interaction with the solution. These applications serve as a bridge between technical aspects and end-users. Alerts are a crucial part of this phase. They make sure that any important events or unusual behavior are quickly brought to the attention of the right people. This proactive approach helps users fix problems before they become serious.

To extract dynamic business insights, tools like Grafana and Microsoft Power BI convert raw data into actionable information, enabling users to analyze data, spot patterns and make real-time decisions. Ultimately, this phase aims to empower users with the tools and knowledge to make informed decisions, leveraging user-friendly dashboards, seamless apps and timely alerts. With easy-to-use dashboards, seamless apps and timely alerts, we make sure users have instant access to the right information for making the best decision.

How can we help you?

At Mediaan Conclusion, we take pride in our team of highly skilled consultants who are ready to pave the path for AIOps integration within your business. With expertise in the technologies covered in this series, they are ready to help you navigate all AIOps components.

Whether you require focused assistance in specific areas or seek a comprehensive end-to-end implementation, don’t hesitate to plan a meeting with our experts. We’re committed to being your trusted partner throughout the process!

Check out our other related AIOps blogs here:

This blog is written in collaboration with Sergej Jurev – AI & ML Engineer at Mediaan Conclusion.