Operations | Monitoring | ITSM | DevOps | Cloud

May 2021

Ways AI is Driving More Efficient Application Performance Monitoring

In the digital age, the speed and performance of apps and websites have a huge impact on the customer experience. To ensure a high level of quality, Application Performance Monitoring (APM) refers to the process of tracking the performance and availability of software systems. Let’s look at what Application Performance Monitoring is, how AI and machine learning are being applied to stay ahead of the competition, and several real-world use cases.

Anomaly Detection with Machine Learning

Unsupervised machine learning can help you detect anomalies in your data and forecast trends. The Elastic Observability and Security solutions have preconfigured machine learning models right out of the box. In this video you will see how you can get started with creating your own machine learning jobs.

Simplifying MLOps with model-driven operators

In early markets such as MLOps, solutions to parts of a large problem arise from multiple open source communities, startups and industry leaders. For the consumer, this entails one problem - integrating pieces of a software puzzle in a maintainable way. Model-driven operators promise a solution by connecting the ops of a single application with declarative integration in a standard that empowers providers.