SkillHack › Career guides › MLOps Engineer
How to become an MLOps Engineer
Model CI/CD, registries, monitoring, and drift detection. Keep the path from training to production repeatable and observed.
- 5phases in the roadmap
- 15topics to work through
- 17graded practice questions
- Freeno payment, ever
The MLOps Engineer roadmap
Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.
Phase 1MLOps foundations
Reproducibility
Reproducibility means the same code, data, and configuration produce the same model and metrics on any machine.
Experiment tracking
Experiment tracking records each training run's parameters, metrics, code version, and output artifact so results are comparable and every deployed model traces back to the run that made it.
Reproducible environments
A reproducible environment guarantees training and serving run with identical dependencies everywhere.
Phase 2MLOps pipelines
Data pipelines
Data pipelines turn raw inputs into clean, versioned datasets on a schedule.
Feature stores
A feature store centralizes feature definitions and materializes them to an offline store for training and a low-latency online store for serving.
Orchestration and scheduling
Orchestration expresses a workflow as a DAG where each task runs only after its dependencies succeed, instead of relying on fragile time-based cron gaps.
Phase 3MLOps deployment
Model packaging and serving
Packaging bundles a trained model with its preprocessing, an inference signature, and pinned dependencies into a versioned artifact (a container image or standard serving format).
Batch vs real-time inference
Choosing batch versus real-time inference follows the freshness and latency the use case actually requires.
Canary and rollout
Progressive delivery ships a new model to a small traffic slice first, compares its live quality and operational metrics against the incumbent, and ramps traffic only while the canary stays healthy.
Phase 4MLOps monitoring
Drift and quality monitoring
Because ground-truth labels often arrive with delay, monitoring input-feature and prediction-score distributions against a training baseline gives the earliest observable signal that a model is…
Logging and tracing
Structured, per-request logging keyed by a correlation id captures each inference's input features, model version, and output score, so any past prediction can be reconstructed and traced end to end.
Cost monitoring
Cost monitoring attributes spend to individual services with tagging and pairs it with utilization metrics, so you can see which deployments are idle or over-provisioned and right-size or autoscale…
Phase 5Reliability and governance
Rollback and recovery
Reliable recovery means being able to instantly restore the previous known-good model when a new version regresses in production.
Model registry and versioning
A model registry stores each model as an immutable, uniquely versioned entry carrying its lineage (data and code references), stage transitions, and approver.
Access and audit
Governing a model that uses sensitive data requires enforced, least-privilege authentication so only authorized services can query it, plus an immutable, complete audit log of who or what accessed…
Reading for this path
The primary sources behind the topics above, all free to read.
- Google: Rules of ML
- MLflow Tracking overview
- Weights & Biases: Experiment tracking
- Docker: Dockerfile best practices
- Google MLOps: CI/CD and automation
- AWS: What is a data pipeline
- dbt: Incremental models
- Feast: Feature store concepts
- Google Vertex AI Feature Store
- Apache Airflow: Core concepts
- Kubeflow Pipelines overview
- KServe: Model serving
- MLflow Models
- AWS SageMaker: deploy models
Coming from another job?
Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.
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Start the MLOps Engineer path for free
The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.
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