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AIOps Fundamentals

Observability + AI for incident response

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Two full lessons from AIOps Fundamentals — exact topics, hands-on lab pairings, same depth as the paid course. Watch the videos free; sign up to access labs + the rest of the curriculum.

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Lesson 0119 min

What AIOps actually means (and what it doesn't)

AIOps is more often a vendor pitch than a working pattern. We separate the genuinely useful applications (anomaly detection, alert correlation, root-cause hints) from the marketing.

What this lesson teaches

· The three real AIOps wins: noise reduction, anomaly detection, RCA hints
· Anti-patterns: 'AI-driven self-healing', 'predictive maintenance' for stateless web apps
· The data prerequisite: structured logs + metrics + traces with consistent IDs
· Build vs buy: when Datadog Watchdog / Splunk ITSI / New Relic AI is enough

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Lesson 0223 min

Anomaly detection on metrics — the practical version

Hands-on walkthrough of building a working anomaly detector on real Prometheus metrics. We use Prophet + a simple confidence-interval threshold and compare to Datadog's offering.

What this lesson teaches

· Time-series decomposition: trend + seasonality + residual
· Prophet vs ARIMA vs simple z-score for ops metrics
· Alert tuning: balancing precision vs recall (and why recall usually wins ops)
· Wiring anomaly alerts into PagerDuty / Slack with severity-aware routing
· Avoiding the 'alert fatigue → ignored alerts → outage' loop

● Paired lab

Train a Prophet model on Prometheus latency metrics from a sample app and trigger Slack alerts.

These are 2 of 28 lessons. Subscribe to @computerpathshala654 for new lessons + course launches. The full 26 remaining lessons are included with cohort enrolment, with a 7-day money-back guarantee.

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