Learn By Doing: Applied ML for AIOps - Anomaly Detection and Forecasting

This hands-on, beginner-friendly AIOps course teaches students how to turn raw operational data into actionable intelligence using machine learning. Designed for SREs, DevOps engineers, platform engineers, observability specialists, and anyone responsible for system reliability, the course provides an end-to-end journey across metrics, anomalies, logs, and forecasting.

Through practical Python notebooks and real operational datasets, learners build intelligent features, detect anomalies with machine learning, cluster logs into meaningful patterns, and forecast future resource usage. By the end of the course, participants will have built a complete AIOps intelligence pipeline ready for real-world operations, including root-cause acceleration, proactive monitoring, and data-driven incident response.

This course is ideal for:

• SRE/DevOps engineers who want to enhance monitoring with AI
• Platform teams building next-generation observability systems
• Data engineers integrating ML into operational pipelines
• Software engineers interested in production analytics
• Beginners who want practical ML experience using real operational datasets

Upon completion, learners will be able to:

• Engineer intelligent features from raw metrics
• Detect anomalies using Isolation Forest and LOF
• Transform messy logs into structured patterns using TF-IDF
• Cluster operational logs with K-Means into meaningful categories
• Forecast resource usage using SARIMA and Exponential Smoothing
• Build dashboards, operational runbooks, and proactive alerting rules
• Apply ML to real production scenarios with confidence

Course Highlights

1. Introduction to AIOps & Intelligent Data Engineering

• Understanding why modern systems require AI-augmented observability
• Limitations of static thresholds and traditional monitoring
• Transforming raw metrics into intelligent features
• Building time-based features (hour-of-day, weekend flags)
• Creating lag features, rolling windows, and trend indicators
• Producing a clean, ML-ready metrics dataset
• Hands-on: Build your intelligent metrics dataset used for all later modules

2. Intelligent Anomaly Detection with Isolation Forest & LOF

• Why anomaly detection is essential for modern operations
• Understanding context-aware detection vs. static thresholds
• Training Isolation Forest on engineered features
• Adding Local Outlier Factor for multi-algorithm validation
• Building confidence scoring and alert prioritization
• Professional train/test split (historical → future)
• Visualizing anomalies on CPU/RAM timelines
• Tuning the contamination parameter for production, staging, and dev
• Hands-on: Build a complete anomaly detection system with confidence scores

3. Log Clustering & Pattern Discovery Using TF-IDF and K-Means

• Challenges of unstructured logs and why rule-based approaches fail
• Extracting patterns from raw log lines
• Converting text to numerical vectors using TF-IDF
• Understanding operational vocabulary from logs
• Applying K-Means to discover meaningful web traffic clusters
• Evaluating clusters using inertia and silhouette scores
• Understanding real cluster meanings: images, static assets, API calls, bots
• Labeling clusters for operational use and assigning team ownership
• Creating a complete cluster documentation package (CSV + insights)
• Hands-on: Produce a production-ready log intelligence report

4. Time-Series Forecasting for Capacity Planning

• Why forecasting prevents outages, bottlenecks, and cost overruns
• Understanding seasonality, trends, and residual patterns
• Training SARIMA and Exponential Smoothing models
• Evaluating predictions with train/test splits, MAE, RMSE, MAPE
• Forecasting CPU, RAM, disk, and network metrics
• Visualizing forecasts with confidence intervals
• Identifying future capacity risks and thresholds
• Hands-on: Build 30-day capacity forecast dashboards for real metrics

5. Final AIOps Automation Project: Intelligent Monitoring Dashboard

• Combine anomalies, log clusters, and forecasts into a single report
• Build a clean operational dashboard summary
• Integrate insights for alerting, investigation, and trend monitoring
• Produce a real AIOps intelligence package ready for team use
• Hands-on: Generate a full AIOps report including anomalies, log patterns, forecasts, and recommendations