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Practical ML Engineering
Learn to build, deploy, and maintain production-grade machine learning systems. From data pipelines to scalable inference.
📊Intermediate
⏱️8 weeks
📚6 Modules
Course Modules
1
ML Pipeline Design
Week 1Data PipelinesFeature EngineeringPipeline Testing
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2
Model Training & Optimization
Week 2-3Hyperparameter TuningDistributed TrainingTraining Tricks
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3
MLOps & Deployment
Week 4-5Model ServingCI/CD for MLFeature Stores
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4
Monitoring & Maintenance
Week 5-6Drift DetectionProduction MonitoringRetraining
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5
Scaling ML Systems
Week 7Horizontal ScalingInference OptimizationCost Management
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6
Production Best Practices
Week 8Model VersioningIncident ResponseDocumentation
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What You'll Build
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ML Pipeline
End-to-end pipeline from raw data to deployed model
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Monitoring Dashboard
Real-time model performance and drift detection system
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Scalable API
Production-ready REST API with load balancing and caching
Tools & Technologies
Python
MLflow
Airflow
FastAPI
Docker
Kubernetes
Prometheus
Grafana
Great Expectations
DVC
BentoML
Feast
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Access all course materials, notebooks, and projects on GitHub
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