This article covers strategies for observing and scaling MLOps infrastructure on Amazon EKS. It details essential metrics for monitoring ML workloads, the hardware landscape, and how to implement Prometheus for effective metrics collection in Kubernetes environments.

This article outlines how to effectively manage alerts using Amazon Managed Service for Prometheus. It covers creating and routing alerting rules, optimizing query performance, and reducing alert fatigue for teams monitoring applications on AWS. Practical examples and YAML configurations are provided for recording and alerting rules.

Salesforce Commerce Cloud successfully transitioned from a self-hosted Prometheus monitoring system to Amazon Managed Service for Prometheus, achieving a 40% reduction in AWS costs while enhancing system reliability and reducing maintenance overhead. This migration allowed the team to focus more on innovation and customer service rather than managing infrastructure. The new solution scales seamlessly across multiple Amazon EKS clusters and regions, consolidating metrics effectively and improving operational efficiency.

Grafana 12 introduces a new feature that allows users to import Prometheus-style alerts and recording rules into Grafana-managed alerts directly through the UI, streamlining the migration process without the need to rewrite existing rules. This functionality enhances compatibility with existing workflows and provides access to Grafana's additional alerting features while preserving the original behavior of Prometheus alerts. Users can easily manage and control the import process, making it easier to transition to Grafana's alerting system.

The blog post discusses the integration of Prometheus and OpenTelemetry, emphasizing the importance of user experience research in observability tools. It highlights the benefits of leveraging OpenTelemetry to enhance monitoring capabilities and improve user satisfaction in software development and operations.

Memory usage in Prometheus can escalate dramatically in enterprise Kubernetes environments due to high-cardinality metrics and labels. This article details methods to analyze and reduce memory consumption effectively, including identifying redundant metrics and employing scripts to optimize monitoring without losing essential data.

Learn how to monitor your Prusa 3D printer using Grafana by leveraging prusa_exporter and Prometheus to visualize printer metrics and set alerts. This setup allows for efficient offline monitoring, even in environments with network restrictions, and offers customization for both hobbyists and developers. The article also discusses challenges in data processing due to the limited resources of embedded systems.