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Machine Learning Engineer
Ship models as reliable, monitored services.
Advanced ~16 weeks·Engineers productionizing ML.
Recommended topic order
Weekly learning plan
Weeks 1–4
- · NumPy/pandas mastery
- · scikit-learn pipelines
Weeks 5–8
- · PyTorch basics
- · Training & evaluation
Weeks 9–12
- · Model serving with FastAPI
- · Batch vs online inference
Weeks 13–16
- · Monitoring & MLOps
- · Dockerize & deploy
Required projects
- Classification service
- Model-serving API
- Batch inference pipeline
Interview topics
VectorizationOverfittingServing latencyFeature pipelinesMonitoring drift
Portfolio expectations
- · A deployed model service with tests, metrics and a model card
Job-readiness checklist
- Builds reproducible training pipelines
- Serves models behind a tested API
- Monitors latency and drift
- Containerizes and deploys
0%ready
Skill-gap analysis
Your live coverage of this path's tracks, from local progress.
Focus areas
Python internalsNumPyPandasScikit-learnPyTorchAPIsModel servingTestingDockerPerformanceMLOpsCloud deployment
Skill prerequisites
data-science