Role roadmaps / MLOps Engineer Role flowchart
Make models a boring production service: CI, registries, feature stores, and the pager. Most of the pain is data platform pain.
Also called: ML Platform Engineer, AI Platform Engineer
You ship: A path from commit to served model with rollback, plus the monitors that catch drift.
After you have operated pipelines — add the model lifecycle
Roadmap Coding Interview Question bank Switch roleData Analyst BI Analyst Data Engineer Analytics Engineer Data Scientist ML Engineer MLOps Engineer AI Engineer Data Architect Nearby roles:ML Engineer , Data Engineer , Data Architect
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Start · MLOps Engineer Foundations Data engineering first Orchestration, warehouses, and tests. MLOps on a broken lake is theater. CI/CD for more than apps Images, seeds, and eval jobs. Git is the source of truth. Cloud primitives IAM, containers, and why a notebook GPU is not a platform. Daily craft Model registry Versions, aliases, and who may promote to prod. Feature store habits Point-in-time joins, online/offline parity, and TTLs. Training pipelines Scheduled retrains, data validation, and a human gate when the metric tanks. How you ship Drift and SLOs Input drift, performance drift, and latency budgets. Page a person. GPU and warehouse cost Idle clusters and feature backfills that scan last year. Career moves MLOps interview Platform sketch, an incident, and “how do you block a bad model?” Next role Architect if you want the whole platform. MLE if you miss training. DE if models were a detour. Keep practicing Study this node
Data engineering first Orchestration, warehouses, and tests. MLOps on a broken lake is theater.
Orchestration, warehouses, and tests. MLOps on a broken lake is theater.
MLOps on a broken lake is theater. Orchestration, warehouses, and tests first.
Time to learn Do not buy a feature store on untested bronze.
Prerequisites Orchestration, warehouse tests Concepts to study The DE roadmap is the prerequisite Feature data is still data Pagers already exist for pipelines — reuse them Tech stacks Airflow / dbt / warehouse — The substrateDE roadmap — The pathHow to study Complete DE foundations before you buy a feature store. What to produce A list of DE skills missing if retraining is folklore Practice on this site Data engineer roadmap; SQL tracks Pitfalls An ML platform on untested bronze Interview prompts What DE skill is missing if retraining is folklore? Links
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