This course covers monitoring and logging tools for IBM Cloud VMware solutions. You will explore the features, functionality, and use cases for each of the three monitoring and logging tools.

This course identifies how to monitor and manage security threats on IBM Cloud.

Learn the essentials of monitoring and observability in development and DevOps. Explore tools and techniques for tracking system performance, identifying bottlenecks, and ensuring the reliability and scalability of software in production environments.

This course will teach you to monitor query executions in Databricks SQL, and also regulate access to query objects. You will learn how to optimize query executions with caches, encrypt queries, and configure alerts based on query results.

Azure Kubernetes Services are a platform-as-a-service offering that provides you with a Kubernetes service in the Azure cloud. This course will teach you how to monitor and troubleshoot AKS Clusters.

You can't manage what you can't measure. In this course, you will learn how to measure key metrics and set up alerts to monitor AWS resources

As you move more of your infrastructure to the cloud, keeping track of everything can be a challenge. This course will teach you how to monitor your CloudFormation stacks using CloudTrail events and available options to view those events.

In this course, you'll learn to monitor web applications and Azure resources using SCOM 2016.

You can add consistent monitoring to your whole application with Docker, the same for every container in every environment. This course teaches you how to expose metrics from Linux and Windows containers, collect them, and display them in dashboards.

Learn how to use monitoring for your Java applications on Azure

Gathering metrics from a variety of systems is challenging if you do it all from scratch. This course will teach you how to load metrics from common systems into Prometheus using exporters so you can spend more time optimizing your applications.

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.