Table of Contents
Days 1-15: Setup & Discovery
What should happen:
- ✦ Access to data sources, compute infrastructure, and code repositories
- ✦ Understanding of existing ML pipeline and model architecture
- ✦ Identification of quick wins and pain points
- ✦ Setup of development environment and local testing
Days 16-30: First Deployment
What should happen:
- ✦ Deploy first model to staging environment
- ✦ Set up basic monitoring (latency, errors)
- ✦ Run comparison against existing baseline
- ✦ Deliver: working model in staging with dashboards
Days 31-60: Monitoring & Optimization
What should happen:
- ✦ Implement data drift and performance monitoring
- ✦ Optimize inference latency and cost
- ✦ Set up alerting for anomalies
- ✦ Deliver: production-ready model with monitoring
Days 61-90: Automation & Scale
What should happen:
- ✦ Build automated retraining pipeline
- ✦ Implement CI/CD for model updates
- ✦ Document runbooks and incident procedures
- ✦ Deliver: end-to-end MLOps pipeline
90-Day Success Metrics
Deployment
Expected Outcome:
Model running in production
Monitoring
Expected Outcome:
Dashboards and alerts active
Documentation
Expected Outcome:
Runbooks completed
Automation
Expected Outcome:
Retraining workflow established
Reliability
Expected Outcome:
Rollback process documented
Warning Signs
Red flags:
- ✦ Day 30: No model deployed (even to staging)
- ✦ Day 60: No monitoring implemented
- ✦ Day 90: Retraining still manual, no automation
- ✦ Avoids infrastructure work, only wants to build models
Who Should See Progress by Day 90
- ✦ Engineering leadership
- ✦ Data science team
- ✦ Platform team
- ✦ Product managers
- ✦ Operations stakeholders
Set Clear Expectations
Senior ML engineers ship and maintain production systems. Set these milestones on day one. Offline Pixel pre-vets senior ML engineers who meet these expectations. Raise a request, talk to candidates, fund the project, and approve payment when the work is done.
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