Data & AI 90 days 2-3 hours/day updated 2026-06-01

AIOps 90-Day Learning Path

Master AIOps in 90 days: anomaly detection, log analytics with ML, predictive alerting, topology mapping, and AI-assisted incident correlation. Cut MTTD and MTTR with intelligent automation.

What AIOps means

AIOps uses machine learning and big data analytics to enhance and automate IT operations. It ingests telemetry from monitoring, logging, and event management systems to detect anomalies, correlate incidents, and predict failures before they impact users. AIOps platforms reduce the noise in operations centers and enable automated remediation, shrinking the gap between detection and resolution.

Who should follow this path

SREs and operations engineers wanting ML-powered observability
DevOps engineers building intelligent monitoring pipelines
Platform engineers scaling observability for large fleets
Data scientists applying ML to operational data
IT operations managers modernizing NOC workflows

Prerequisites

Hands-on experience with monitoring tools (Prometheus, Datadog, or Splunk)
Basic Python and pandas/scikit-learn familiarity
Understanding of log formats and time-series data
Familiarity with Kubernetes and cloud infrastructure
Basic statistics (anomaly detection, distributions)

The 90-day plan

Daily study recommendation: 2-3 hours/day, six days a week. Consistency beats intensity — block the time in your calendar like a meeting.

Days 1–15: Foundation

AIOps definition, capabilities, and vendor landscape
Observability pillars: metrics, logs, traces, events
Telemetry data types and ingestion patterns
Gartner AIOps Market Guide overview
Manual vs automated NOC workflows comparison

Outcome: Articulate the AIOps value proposition and map telemetry sources to AIOps use cases.

Days 16–30: Core concepts

Time-series anomaly detection (statistical and ML methods)
Log analytics with ML using Elastic ML or Splunk MLTK
Topology and dependency mapping
Event correlation and noise reduction
Baseline modeling and seasonal trend detection

Outcome: Implement ML-based anomaly detection on real time-series metrics and log data.

Days 31–45: Tools and workflows

AIOps platforms: Dynatrace Davis AI, Moogsoft, BigPanda
Predictive alerting and forecasting
Alert correlation and deduplication
CMDB integration for topology context
Integration with ITSM tools (ServiceNow, PagerDuty)

Outcome: Configure an AIOps platform to correlate alerts and reduce noise by at least 50% in a test environment.

Days 46–60: Hands-on projects

Root cause analysis automation
Natural language processing for log analysis
Runbook automation and self-healing systems
AI-assisted incident routing
Feedback loops for model retraining

Outcome: Build an automated root cause analysis workflow with runbook-driven self-healing responses.

Days 61–75: Advanced practices

Capacity planning with ML forecasting
FinOps integration: cost anomaly detection
SLO prediction and proactive alerting
Multi-cloud AIOps architecture
Explainability for AIOps models (why was this alert fired)

Outcome: Implement predictive capacity planning and SLO-aware alerting across a multi-cloud environment.

Days 76–90: Portfolio, interview & certification prep

AIOps portfolio project
Preparing for Dynatrace Professional certification
AIOps interview questions and use-case scenarios
Metrics: alert noise reduction, MTTD, MTTR improvements
Emerging topics: LLM-based ops assistants and AIOps copilots

Outcome: Complete an AIOps portfolio project and be ready for AIOps engineer and senior SRE interviews.

Weekly outcomes at a glance

Phase	Outcome
Days 1–15	Articulate the AIOps value proposition and map telemetry sources to AIOps use cases.
Days 16–30	Implement ML-based anomaly detection on real time-series metrics and log data.
Days 31–45	Configure an AIOps platform to correlate alerts and reduce noise by at least 50% in a test environment.
Days 46–60	Build an automated root cause analysis workflow with runbook-driven self-healing responses.
Days 61–75	Implement predictive capacity planning and SLO-aware alerting across a multi-cloud environment.
Days 76–90	Complete an AIOps portfolio project and be ready for AIOps engineer and senior SRE interviews.

Tools to learn

Dynatrace
Datadog
Moogsoft
BigPanda
Elastic (ML features)
Splunk MLTK
Prometheus
OpenTelemetry
PagerDuty
Grafana
Python (scikit-learn, statsmodels)
Apache Kafka

Labs to practice

Mini projects

Build a time-series anomaly detection pipeline using Prometheus metrics + Python scikit-learn, alerting via PagerDuty
Deploy an Elastic ML job to detect anomalous patterns in Kubernetes pod logs
Create a noise-reduction dashboard correlating alerts from multiple sources into incidents using BigPanda

Interview questions to prepare

What is AIOps and how does it differ from traditional monitoring?
Explain three anomaly detection algorithms suitable for time-series metrics.
How do you measure the effectiveness of an AIOps implementation?
What is event correlation and how does it reduce alert noise?
How would you build a self-healing system that auto-restarts failed Kubernetes pods?
Explain the difference between reactive, proactive, and predictive IT operations.
How do you prevent model drift in production AIOps anomaly detectors?
What data sources are most valuable for AIOps and why?

Certification suggestions

Dynatrace Professional Certification — Dynatrace
Datadog Fundamentals — Datadog Learning Center
Splunk Core Certified Power User — Splunk
ITIL 4 Foundation — DevOps School

Browse the full certification registry for exam details and official links.

Free resources

// instructor-led option

Prefer live, guided training with mentors and certification support? DevOpsSchool.com runs paid instructor-led programs that pair well with this free path.

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