# MLOps Engineer Roadmap

MLOps sits between platform engineering and ML: you enable teams to ship models reliably.

## What gets evaluated

- deployment strategies for models
- monitoring and drift
- feature stores and data pipelines
- reproducibility and governance
- platform design and developer experience

## Core pillars

1. Software + platform engineering fundamentals
2. ML production readiness (tests, monitoring, rollback)
3. Data reliability (freshness, lineage)

## 12-week plan

Weeks 1–3: Infra foundations

- containers, Kubernetes, CI/CD, observability

Weeks 4–6: ML production basics

- training/serving skew, feature freshness, deployment patterns

Weeks 7–9: Platform design

- multi-tenant pipelines, quotas, cost control

Weeks 10–12: Mock loops

- platform/system design + behavioral

## Failure modes

- treating MLOps as “just Kubernetes”
- no ML-specific monitoring/testing plan
- weak story on how the platform improves team velocity and safety

## Archetype adjustments

- **FAANG/high-growth:** platform scale and experimentation tooling.
- **Enterprise:** governance, approvals, model risk management.
- **Automotive/industrial:** safety, verification, traceability.