Overview
What this challenge is about.
Reconstruct a failed ML deployment timeline, run a blameless root-cause analysis, and propose three corrective actions. Earn your verifiable certificate.
The scenario
The telco (around 38M subscribers, 24 contact centres globally) is one of the consultancy's top-3 accounts and the relationship is at risk if the post-mortem doesn't land.
The Brief
What you'll do, and what you'll demonstrate.
Run a blameless post-mortem on a failed ML deployment and recommend three corrective actions that will land with both engineering and the business sponsor.
Earning criteria — what you'll demonstrate
- Run a blameless post-mortem across data, model, and process axes
- Reconstruct an ML deployment timeline from heterogeneous artifacts
- Recommend corrective actions that survive both engineering and business scrutiny
- Present ML failures to mixed audiences without losing either side
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Root Cause Analysis
Apply root cause analysis to solve real industry problems and demonstrate production-level capability.
- Stakeholder Framing
Apply stakeholder framing to solve real industry problems and demonstrate production-level capability.
- Model Monitoring
Apply model monitoring to solve real industry problems and demonstrate production-level capability.
- Ml Pipelines
Apply ml pipelines to solve real industry problems and demonstrate production-level capability.
- Deployment
Apply deployment to solve real industry problems and demonstrate production-level capability.
- Case Study Analysis
Apply case study analysis to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Solutions Architect
Running mixed-audience post-mortems on failed deployments is one of the most senior solutions-architect competencies and is exactly what consultancies hire for.
This challenge sharpens
- root-cause-analysis
- stakeholder-framing
- deployment
MLOps Engineer
Reconstructing a failure timeline from logs and proposing monitoring/process fixes is core MLOps incident-response work.
This challenge sharpens
- model-monitoring
- ml-pipelines
- deployment
AI Product Manager
Owning a corrective-action plan that engineering and the business sponsor both sign off on is the AI-PM's post-incident job.
This challenge sharpens
- stakeholder-framing
- case-study-analysis
- root-cause-analysis