Table of Contents
Many ML engineers list 'production ML' on their resume. Few have actually shipped models that serve real traffic, handled failures, and stayed online for months. Here's how to separate production experience from notebook projects.
Red Flags in Resumes
Skeptical of:
- ✦ "Production" experience but no mention of scale (requests/second, data volume)
- ✦ No monitoring, alerting, or observability mention
- ✦ No mention of rollbacks or incident handling
- ✦ Only Jupyter notebooks in GitHub portfolio
- ✦ No CI/CD or deployment pipeline experience
What to Look For in a Portfolio
Signs of real production experience:
- ✦ Deployed model with public API endpoint
- ✦ GitHub repo with Dockerfile, CI/CD config, tests
- ✦ Monitoring dashboards (even screenshots)
- ✦ Documentation of rollback procedures
- ✦ Post-mortems of production incidents
Evidence of Real Production Experience
- ✦ Latency targets
- ✦ Traffic volume metrics
- ✦ Incident response examples
- ✦ Monitoring dashboards
- ✦ Deployment pipelines
- ✦ Rollback procedures
Questions to Ask
Look for honesty, root cause analysis, and concrete remediation steps. Generic answers or 'never failed' are red flags.
Numbers required. No numbers = likely low scale or not real production.
Verify Every Production Claim
Built model
Follow-up Question:
How was it deployed?
Production system
Follow-up Question:
How many requests per second?
Monitoring
Follow-up Question:
What alerts existed?
Scalability
Follow-up Question:
Largest traffic spike handled?
Reliability
Follow-up Question:
Biggest outage experienced?
Separate Real from Fake
Production ML experience is rare and valuable. Verify claims with specific questions about scale, failures, and operations. Offline Pixel pre-vets production ML experience before you interview. Raise a request, talk to candidates, fund the project, and approve payment when the work is done.
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