Six Building Blocks Behind Successful AI-powered Operations

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.

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 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.

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.