Organizations of all sizes and across all industries gather and analyze metrics or key performance indicators (KPIs) to help their businesses run effectively and efficiently. Operational metrics are used to evaluate performance, compare results, and track relevant data to improve business outcomes. For example, you can use operational metrics to determine application performance (the average time it takes to render a page for an end user) or application availability (the duration of time the application was operational). One challenge that most organizations face today is detecting anomalies in operational metrics, which are key in ensuring continuity of IT system operations. Traditional rule-based methods are manual and look for data that falls outside of numerical ranges that have been arbitrarily defined.
Artificial intelligence for IT operations (AIOps) combines sophisticated methods from deep learning, data streaming processing, and domain knowledge to analyse infrastructure data from internal and external sources to automate operations and detect anomalies (unusual system behavior) before they impact the quality of service. Odej Kao, professor at the University of Technology Berlin, gave a keynote presentation about artificial intelligence for IT operations at DevOpsCon Berlin 2021. In data stream processing we frequently struggle to find sufficient amounts of data. On the other hand, in AIOps we have many different sources (e.g., metric, logs, tracing, events, alerts) with several Terabytes of data produced in a typical IT infrastructure per day. We utilize the power of these hidden gems to assist DevOps administrators and jointly with the AI-models improve the availability, security, and the performance of the overall system.
Serverless computing has become a next-generation public cloud service that auto-scales and charges only when used. If you are diving into Serverless or migrating from a server-based to serverless architecture, these serverless monitoring tools will help you to examine the security, track the serverless architecture, pinpoint and troubleshoot threats. You will find that these Serverless monitoring platforms offer strong support and easy to access documentation to their users to overcome obstacles. So, without further ado, let's get started. These Serverless Monitoring Tools provide rich features for the seamless integrations as well as Infrastructure for monitoring health, status check, verify logs, debugging, and much more.
Anomaly Detection is an API built with Azure Machine Learning that is useful for detecting different types of anomalous patterns in your time series data. The API assigns an anomaly score to each data point in the time series, which can be used for generating alerts, monitoring through dashboards or connecting with your ticketing systems. The machine learning based API enables: - *Flexible and robust detection:* The anomaly detection models allow users to configure sensitivity settings and detect anomalies among seasonal and non-seasonal data sets. Users can adjust the anomaly detection model to make the detection API less or more sensitive according to their needs. This would mean detecting the less or more visible anomalies in data with and without seasonal patterns.