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Sources and specific examples on hand when peers push back

$199.00
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A tailored course, built for your situation

Sources and specific examples on hand when peers push back

Build unshakable technical reasoning in high-stakes system design reviews

$199 one-time
24-hour access provisioning 30-day money-back guarantee Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.

Who this is for

Senior SREs leading architecture reviews and system design validation in high-velocity environments

Who this is not for

Engineers focused only on execution without influencing design decisions or defending technical trade-offs

What you walk away with

  • Cite real-world incidents and published thresholds to back architectural recommendations
  • Walk into design reviews with pre-mapped examples from top-tier post-mortems
  • Articulate trade-offs using language adopted by FAANG-level SRE teams
  • Deflect challenge with precision, not volume, using documented precedents
  • Ship consensus-driven designs faster by reducing rework from late-stage pushback

The 12 modules (with all 144 chapters)

Module 1. Grounding decisions in documented outages
Learn how to reference real incidents like Cloudflare’s the current cycle outage or AWS Kinesis degradation to justify redundancy thresholds.
12 chapters in this module
  1. Using outage post-mortems as precedent
  2. Matching latency budgets to real user impact
  3. How Google defines 'acceptable degradation'
  4. When to invoke CAP theorem in trade-offs
  5. Citing SLI definitions from public sources
  6. Mapping error budget burn to real downtime
  7. Justifying observability spend with examples
  8. Referencing incident response timelines
  9. Benchmarking recovery targets
  10. Calling out false trade-offs
  11. Avoiding hypotheticals in design reviews
  12. Pre-loading examples before review
Module 2. Chaos engineering thresholds from top teams
Adapt Netflix and AWS practices to defend your testing scope and blast radius decisions.
12 chapters in this module
  1. Netflix’s rules for blast radius
  2. When to stop a chaos experiment
  3. Defining 'safe failure' in staging
  4. Chaos vs monitoring false confidence
  5. Documenting pre-test checks
  6. How AWS tests region failover
  7. Using failure injection to prove resilience
  8. Setting roll-back triggers in advance
  9. Aligning experiments to RTO
  10. Explaining risk reduction math
  11. Chaos testing cadence benchmarks
  12. Sharing results with non-technical leads
Module 3. SLI and SLO decision patterns
Defend your definitions using concrete sources from Google SRE book and public platform SLAs.
12 chapters in this module
  1. Defining 'good request' for GraphQL
  2. How Google measures availability
  3. When to use p99 vs median
  4. SLI choices that survive audits
  5. Avoiding vanity metrics in SLOs
  6. Using user journeys to define success
  7. Adjusting for feature rollout phases
  8. Documenting exceptions cleanly
  9. Handling edge case traffic spikes
  10. Tying error budgets to business cycles
  11. Justifying alerting thresholds
  12. Responding to SLO violation reviews
Module 4. Vendor lock-in trade-offs with evidence
Use documented migrations and exit costs from Atlassian, Shopify, and Dropbox to back platform decisions.
12 chapters in this module
  1. When to accept managed service limits
  2. Cost of migrating from Firebase
  3. Lessons from Dropbox’s infrastructure shift
  4. Multi-cloud complexity benchmarks
  5. Justifying K8s investment with data
  6. Documenting egress cost risks
  7. Open source vs managed trade-offs
  8. License lock-in red flags
  9. Avoiding API deprecation surprises
  10. Planning for abstraction layers
  11. Benchmarking portability efforts
  12. Citing compliance exit scenarios
Module 5. Capacity planning with public benchmarks
Defend headroom decisions using ingestion rates from GitHub, Slack, and Uber.
12 chapters in this module
  1. How Slack handles message bursts
  2. GitHub’s rate limiting patterns
  3. Uber’s surge capacity design
  4. Using p99.9 for planning
  5. Justifying auto-scaling triggers
  6. Documenting peak season loads
  7. Citing cold-start penalties
  8. Avoiding over-provisioning guilt
  9. Linking cost to reliability tier
  10. Explaining headroom to finance
  11. Responding to efficiency audits
  12. Validating forecast models
Module 6. Observability scope decisions
Justify logging, tracing, and metrics coverage using DORA and Gartner benchmarks.
12 chapters in this module
  1. What DORA says about log retention
  2. When distributed tracing pays off
  3. Setting sampling rates with purpose
  4. Avoiding telemetry overload
  5. Linking observability depth to MTTR
  6. Using golden signals to cut noise
  7. Defining 'sufficient' context
  8. Balancing cost and trace depth
  9. Proving value of structured logging
  10. Responding to data privacy limits
  11. Choosing which systems to trace
  12. Benchmarking alert fatigue reduction
Module 7. Incident response role clarity
Clarify escalation paths and on-call scope using documented org models from PagerDuty and Zendesk.
12 chapters in this module
  1. Defining 'first responder' clearly
  2. When to escalate to SRE
  3. Using runbook maturity levels
  4. Avoiding role confusion in war rooms
  5. Setting incident commander authority
  6. Documenting decision logs
  7. Justifying post-mortem depth
  8. Reducing duplicate comms
  9. Citing MTTR benchmarks
  10. Aligning comms to stakeholder level
  11. Managing executive visibility
  12. Responding to audit requests
Module 8. Automated rollback criteria
Defend your deployment safety rules using thresholds from GitLab, CircleCI, and Shopify.
12 chapters in this module
  1. Failure rate triggers for rollback
  2. When to halt a canary
  3. Defining 'safe' metric degradation
  4. Linking rollback to user impact
  5. Avoiding manual intervention delays
  6. Documenting silent failures
  7. Using error budget burn as signal
  8. Setting pre-deployment checks
  9. Citing CI/CD pipeline standards
  10. Explaining automation logic
  11. Responding to rollback audits
  12. Tracking rollback success rates
Module 9. Security and reliability trade-offs
Use documented cases from Okta and LastPass to justify auth and access patterns.
12 chapters in this module
  1. Balancing MFA friction and uptime
  2. When to enforce IP allowlists
  3. Documenting compliance-driven outages
  4. Avoiding auth cascades
  5. Citing zero-trust migration costs
  6. Justifying certificate rotation windows
  7. Defending passwordless timelines
  8. Linking auth design to SLOs
  9. Responding to SOC2 findings
  10. Explaining audit paths to developers
  11. Managing third-party auth risks
  12. Benchmarking session timeout policies
Module 10. Disaster recovery realism
Back up your RTO and RPO claims with documented recovery times from Azure and GCP outages.
12 chapters in this module
  1. How Google recovered from region loss
  2. Defining 'realistic' RTO
  3. Documenting data recovery steps
  4. Avoiding over-optimistic claims
  5. Citing backup verification frequency
  6. Justifying cross-region sync costs
  7. Testing failover without disruption
  8. Responding to audit findings
  9. Linking recovery time to SLA
  10. Managing stakeholder expectations
  11. Tracking drill success metrics
  12. Explaining data consistency trade-offs
Module 11. Technical debt prioritization
Use quantified benchmarks from Honeycomb and Stripe to defend refactoring scope and timing.
12 chapters in this module
  1. When to retire legacy endpoints
  2. Citing incident root cause frequency
  3. Measuring refactoring ROI
  4. Avoiding rewrite traps
  5. Linking debt to MTTR
  6. Justifying documentation effort
  7. Defining 'critical' tech debt
  8. Responding to velocity pressure
  9. Tracking incident recurrence
  10. Benchmarking rework costs
  11. Using error rates to prioritize
  12. Explaining long-term cost savings
Module 12. Design review facilitation
Lead reviews with confidence using structured patterns from Amazon and Microsoft.
12 chapters in this module
  1. Setting decision criteria upfront
  2. Avoiding consensus traps
  3. Using RFCs to drive closure
  4. Defining 'controversial' clearly
  5. Citing precedent over opinion
  6. Justifying chosen patterns
  7. Responding to scope creep
  8. Documenting rejected options
  9. Aligning to reliability standards
  10. Explaining reasoning succinctly
  11. Reducing rework cycles
  12. Earning peer trust over time

How this maps to your situation

  • During high-pressure architecture review
  • When defending SLO definitions to product teams
  • After a major incident with external scrutiny
  • Before rolling out a new observability pipeline

Before vs. after

Before
Having to defend design choices with general principles and internal assumptions
After
Walking into reviews with documented examples and cited precedents from industry leaders

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 2 hours per module, designed to be completed alongside active design cycles.

How this compares to the alternatives

Most SRE courses focus on tools or compliance checklists. This course is different: every chapter builds your ability to defend decisions using real-world engineering outcomes and published patterns, not theory.

Frequently asked

Is this course about tools like Prometheus or Terraform?
No. This course focuses on the reasoning and examples used in high-stakes design discussions, how to defend decisions using precedent, not which tool to pick.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me lead architecture reviews?
Yes. The course builds your ability to reference real incidents, define trade-offs clearly, and earn peer consensus without over-negotiating.
$199 one-time. Approximately 2 hours per module, designed to be completed alongside active design cycles..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee· 144 chapters· Hand-built playbook included· Account access within 24 hours
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