Role roadmaps / MLOps Engineer Role roadmap
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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Terminal Start · Station 1 of 16
Click to stop Start · MLOps Engineer A path from commit to served model with rollback, plus the monitors that catch drift. Inspect station brief →
Checkpoint · Station 2 of 16
Click to stop Foundations The tools you cannot fake in week one Inspect station brief →
Bus Stop · Station 3 of 16
Click to stop Data engineering first Orchestration, warehouses, and tests. MLOps on a broken lake is theater. Open Syllabus →
Bus Stop · Station 4 of 16
Click to stop CI/CD for more than apps Images, seeds, and eval jobs. Git is the source of truth. Open Syllabus →
Bus Stop · Station 5 of 16
Click to stop Cloud primitives IAM, containers, and why a notebook GPU is not a platform. Open Syllabus →
Checkpoint · Station 6 of 16
Click to stop Daily craft What you actually do on a Tuesday Inspect station brief →
Bus Stop · Station 7 of 16
Click to stop Model registry Versions, aliases, and who may promote to prod. Open Syllabus →
Bus Stop · Station 8 of 16
Click to stop Feature store habits Point-in-time joins, online/offline parity, and TTLs. Open Syllabus →
Bus Stop · Station 9 of 16
Click to stop Training pipelines Scheduled retrains, data validation, and a human gate when the metric tanks. Open Syllabus →
Checkpoint · Station 10 of 16
Click to stop How you ship Production habits, not extra courses Inspect station brief →
Bus Stop · Station 11 of 16
Click to stop Drift and SLOs Input drift, performance drift, and latency budgets. Page a person. Open Syllabus →
Bus Stop · Station 12 of 16
Click to stop GPU and warehouse cost Idle clusters and feature backfills that scan last year. Open Syllabus →
Checkpoint · Station 13 of 16
Click to stop Career moves How this role talks in interviews Inspect station brief →
Bus Stop · Station 14 of 16
Click to stop MLOps interview Platform sketch, an incident, and “how do you block a bad model?” Open Syllabus →
Bus Stop · Station 15 of 16
Click to stop Next role Architect if you want the whole platform. MLE if you miss training. DE if models were a detour. Open Syllabus →
Final Destination · Station 16 of 16
Click to stop Keep practicing SQL + Python for this role Inspect station brief →
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 Phase 1: 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. Phase 2: 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. Phase 3: 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. Phase 4: 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.
Data engineering first Orchestration, warehouses, and tests. MLOps on a broken lake is theater.
Checked automatically: you solved the linked practice problems.
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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