AIOps in Practice - Logging and Alerting at Scale

This course is a practical, hands-on guide to building a robust logging and alerting system, a critical foundation for any AIOps practice. You will move beyond theory and learn to deploy, manage, and utilize a modern observability stack using industry-standard open-source tools.

The course focuses on the second pillar of observability logs and how to transform them from a simple data source into a powerful tool for proactive monitoring and intelligent alerting. You will learn to centralize  logs, query them efficiently, and, most importantly, create an alerting system that provides clear signals instead of overwhelming noise.

The key technologies you will master include:

• Grafana Loki: A highly efficient, cost-effective log aggregation system.
• Grafana Alloy: A modern telemetry collector for discovering and forwarding    logs.
• Prometheus: The de facto standard for metrics monitoring and firing alerts.
• Alertmanager: A powerful tool to deduplicate, group, and route alerts.
• Docker Compose: To orchestrate the entire observability stack.

Lab Content Breakdown:

The course is structured around a series of hands-on labs that progressively build your skills:

Lab 1: Introduction to Grafana Loki

This introductory lab sets the stage by explaining the "why" behind modern log management. You will learn the fundamental concepts of Grafana Loki and its advantages over traditional, more costly logging solutions.

Objectives:

• Understand the core problems Loki solves.
• Learn about Loki's architecture, which indexes metadata (labels) rather than full-text content, making it highly efficient.
• Get an overview of different installation and deployment methods.
• Key Concepts: Log aggregation, label-based indexing, cost-effectiveness, and the overall LGTM (Loki,

      Grafana, Tempo, Mimir) stack.

Lab 2: Centralized Logging with Grafana Loki

In this lab, you get hands-on and build a complete, centralized logging pipeline    from scratch. You will deploy and configure the entire stack needed to collect, store, and visualize logs from a sample application.

Objectives:

• Deploy Loki, Grafana Alloy, and a sample application using Docker Compose.
• Configure Alloy to automatically discover container logs.
• Implement structured logging in a Python Flask application.
• Set up Grafana to query and explore your logs.
• Key Concepts: Docker Compose for orchestration, structured vs. unstructured logging, Alloy configuration for log discovery and processing, and the importance of label design to avoid high cardinality.

Lab 3: Querying Logs with LogQL

This lab focuses on LogQL, Loki's powerful and flexible query language. You will learn how to effectively search and analyze your logs to extract meaningful information, moving from simple queries to advanced analysis.

Objectives:

• Master LogQL syntax, which is inspired by Prometheus's PromQL.
• Filter log streams using label selectors.
• Perform full-text searches on log content using line filters.
• Use parser expressions (json, logfmt) to extract structured data from log lines for more powerful filtering and aggregation.
• Key Concepts: Log stream selectors, line filters, and parser expressions.

Lab 4: Correlating Metrics and Logs

This lab demonstrates one of the most powerful features of a unified observability platform: the ability to seamlessly pivot between metrics and logs. You will learn how to correlate data to dramatically speed up root cause analysis.

Objectives:

• Understand the power of using consistent labels across metrics (Prometheus) and logs (Loki).
• Configure Grafana to create "data links" that allow you to jump from a metric graph (e.g., a spike in errors) directly to the relevant logs for that exact time period.
• Build unified dashboards that combine metrics and logs in a single view.
• Key Concepts: Unified observability, data correlation, and reducing Mean Time to Diagnosis (MTTD).

Lab 5: Intelligent Alerting with Prometheus Alertmanager

This final lab tackles the critical challenge of alert fatigue. You will learn to use Alertmanager to transform a noisy stream of alerts into a manageable, actionable set of notifications. This is where you bring the "intelligent" part of AIOps to your alerting strategy.

Objectives:

• Write effective Prometheus alert rules based on metrics.
• Deploy Alertmanager and configure it to receive alerts from Prometheus.
• Use grouping to consolidate hundreds of similar alerts into a single notification.
• Implement inhibition rules to suppress downstream alerts during a major outage, allowing you to focus on the root cause.
• Configure silencing for planned maintenance windows.
• Design a multi-team routing tree to send the right alerts to the right teams via the right channels(e.g., Slack vs. PagerDuty).
• Key Concepts: Alert fatigue, grouping, inhibition, silencing, and routing. By the end of this lab, you'll be able to build an alerting system that engineers will thank you for.