AIOps Learning Roadmap

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Stage 1: Foundation — Refresh Your Core

Goal: Understand how AIOps extends traditional DevOps/CloudOps.

Learn:

  • What is AIOps?

    • Key capabilities: anomaly detection, event correlation, root cause analysis, predictive alerts, automation.

    • Gartner’s 3 pillars: Monitoring + Machine Learning + Automation.

  • Differences between DevOps, MLOps, and AIOps.

  • AIOps Lifecycle:

    • Data collection → Normalization → Analysis → Correlation → Automation → Continuous Learning.

Hands-on:

  • Explore AIOps case studies (e.g., ServiceNow, Dynatrace, Moogsoft, Datadog).

  • Read whitepapers by Gartner or IBM on “The Evolution of AIOps.”

Stage 2: Data & Observability Foundation

Goal: Learn how to collect and process data for AI-driven insights.

Learn:

  • Telemetry Data Types: Metrics, logs, traces, events.

  • Observability Stack:

    • Prometheus, Grafana, Loki, OpenTelemetry, Elasticsearch, Fluentd, Kibana (EFK), Jaeger.

  • Data Pipelines for AIOps:

    • Kafka, Spark, or Fluent Bit for real-time data streaming.

    • Data normalization and enrichment techniques.

Hands-on:

  • Build a small observability pipeline:

    • Use OpenTelemetry to collect metrics from microservices.

    • Send them to Prometheus + Grafana.

    • Store logs in Elasticsearch.

Stage 3: Machine Learning for Operations

Goal: Apply ML models to IT Ops problems.

Learn:

  • ML Concepts (focus on applied ML):

    • Supervised vs unsupervised learning.

    • Time-series forecasting (ARIMA, LSTM).

    • Anomaly detection (Isolation Forest, Autoencoders, Prophet).

    • Clustering (K-means, DBSCAN) for event correlation.

  • Python libraries:

    • Scikit-learn, Pandas, Numpy, PyTorch (basic understanding), Prophet.

Hands-on:

  • Train an anomaly detection model using sample metrics.

  • Build a “predictive alert” using time-series forecasting.

  • Implement event correlation on synthetic log data.

Stage 4: Automation & Self-Healing

Goal: Automate the incident response lifecycle.

Learn:

  • Event-driven automation with:

    • AWS Lambda / Azure Logic Apps / GCP Cloud Functions

    • Ansible + AI-triggered workflows

    • ChatOps integration (Slack/Teams + AI bot notifications)

  • Closed-loop remediation:

    • Integrate ML alerts → trigger Terraform/Ansible jobs → fix infra automatically.

Hands-on:

  • Create a workflow where:

    • CPU anomaly triggers a predictive alert.

    • Lambda automatically scales ECS service or restarts a pod.

Stage 5: AIOps Platforms & Tools

Goal: Learn real-world enterprise tools.

Popular Tools to Explore:

CategoryTools
Monitoring & AIOpsDynatrace, Datadog, New Relic, Moogsoft, BigPanda, Splunk ITSI, ServiceNow AIOps
AutomationStackStorm, Rundeck, Ansible, Terraform
Cloud-Native AI OpsAzure Monitor + ML, AWS DevOps Guru, GCP Operations Suite
Custom AI PipelinesKubeflow, MLflow, Airflow, Prometheus + Python models

Hands-on:

  • Use AWS DevOps Guru or Azure AIOps to detect anomalies.

  • Experiment with Dynatrace Davis AI or ServiceNow AIOps in trial mode.

Stage 6: Integrate AIOps with MLOps

Goal: Build synergy between AIOps and MLOps pipelines.

Learn:

  • How AIOps can support MLOps (e.g., model monitoring, drift detection).

  • Using MLflow for tracking AI models used in operations.

  • Infrastructure monitoring for ML workloads (GPU usage, data drift).

Hands-on:

  • Create a pipeline to monitor ML model performance metrics and trigger retraining alerts.

Stage 7: Advanced Topics & Enterprise Readiness

Goal: Architect enterprise-grade AIOps solutions.

Learn:

  • AIOps architecture patterns:

    • Data lake + ML engine + automation layer.

  • Scaling AIOps:

    • Handling multi-cloud data.

    • Governance, compliance (GDPR, SOC2), and data privacy in AIOps.

  • Integrating with ITSM:

    • ServiceNow / Jira for incident correlation and RCA automation.

Hands-on:

  • Design an AIOps reference architecture for your organization (HLTH Group, for instance).

  • Demonstrate how anomalies trigger automated RCA reports and self-healing actions.

Stage 8: Certifications & Continuous Learning

Certifications to Consider:

  • IBM AIOps & Automation (official path)

  • Dynatrace Certified Associate

  • AWS Certified Machine Learning – Specialty

  • Microsoft Certified: Azure AI Engineer Associate

Follow these communities:

  • AIOps Exchange

  • GitHub projects: “OpenAIOps,” “Prometheus Anomaly Detection”

  • LinkedIn groups on AIOps and Intelligent Automation

📚 Learning Resources

  • Books:

    • AIOps: Real-World Use Cases in Machine Learning for IT Operations — by Jason Bloomberg.

    • Intelligent Automation: Welcome to the World of Hyperautomation — Pascal Bornet.

  • Courses (Recommended Path):

    1. Coursera: IBM AIOps Fundamentals

    2. Udemy: AI for IT Operations Engineers

    3. Pluralsight: AI and Machine Learning for Infrastructure Monitoring

    4. AWS SkillBuilder: DevOps Guru Deep Dive

🧠 Outcome After Completion

You’ll be able to:

  • Design and implement self-healing, predictive infrastructures.

  • Automate RCA and incident management.

  • Integrate AIOps into DevOps + CloudOps pipelines.

  • Architect enterprise-grade AIOps platforms.

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