Flagship course
Edge QoS Analytics Studio
A six-week studio for engineers and analysts who need application analytics for edge computing service quality—scorecards, drift stories, and stakeholder-ready narratives.
Ask about the next cohort
Learning outcomes
- Define an application-level QoS model that spans edge hops, not only origin availability.
- Detect and narrate latency drift with baselines that survive seasonal traffic.
- Reconcile RUM, synthetic, and infra metrics into one confidence-aware board.
- Brief product and operations leaders with a weekly quality memo format.
Modules
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1
Quality vocabulary for edge apps
Map user journeys to hops, choose signals that matter, and retire vanity gauges.
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2
Scorecard architecture
Weights, windows, and rollups that stay readable when you add a new region.
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3
Latency drift labs
Hands-on detection, false-positive hunting, and Korea metro case datasets.
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4
When telemetry disagrees
Conflict trees, confidence notes, and escalation paths that avoid blame theater.
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5
Stakeholder storytelling
Turn panels into decisions: budget asks, freeze recommendations, and rollback cues.
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6
Studio showcase
Present your scorecard critique and receive structured peer feedback.
FAQ
Do I need a production edge fleet to join?
Helpful, but not required. We provide anonymized datasets. Teams without live access still complete the studio, though some exercises will feel more abstract.
Which tools do you cover?
Concepts transfer across common stacks. Labs use open sample CSVs and a lightweight notebook—no vendor lock-in lectures.
What is a real limitation of this course?
We do not deep-dive into proprietary probe firmware or carrier-grade packet brokers. If your bottleneck is hardware calibration, pair this studio with vendor training—our focus stays on application analytics and decision design.
Can my whole team enroll?
Yes. For five or more seats, ask about the Fleet Scorecard Intensive for private facilitation.
Reviews from this course
“Week five’s stakeholder memo template replaced our 40-slide monthly review. Leadership finally asks about drift windows instead of raw p99 spikes.”
Jiwoo K., SRE — “Clear labs. The Korea metro dataset matched our Seoul storefront edges closely enough to reuse thresholds.”