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MLOps
Learn to deploy, monitor, and maintain machine learning models in production with modern MLOps practices.
10 weeks
12 Modules
Certificate Included
Learning Outcomes
What You'll Learn
By the end of this course, you'll be able to:
Build automated ML pipelines
Deploy models to cloud platforms
Monitor model performance
Implement CI/CD for ML
Handle model updates
Manage ML infrastructure
Curriculum
Course Modules
A comprehensive curriculum designed for practical application.
1
MLOps Introduction
- What is MLOps?
- ML lifecycle
- MLOps maturity
- Tool landscape
2
Experiment Tracking
- MLflow
- Weights & Biases
- Experiment organization
- Artifact management
3
Data Pipelines
- ETL pipelines
- Feature engineering
- Data validation
- Data versioning
4
Model Training Pipelines
- Pipeline orchestration
- Hyperparameter tuning
- Distributed training
- Reproducibility
5
Model Registry
- Model versioning
- Staging
- Promotion
- Metadata tracking
6
Model Serving
- REST APIs
- Batch inference
- Streaming
- ONNX export
7
Containerization
- Docker for ML
- GPU containers
- Model packaging
- Serverless
8
Cloud Deployment
- AWS SageMaker
- Azure ML
- Google Vertex
- Kubernetes
9
Monitoring
- Model metrics
- Data drift
- Concept drift
- Alerting
10
CI/CD for ML
- Testing ML code
- Automated retraining
- A/B testing
- Rollbacks
11
Feature Stores
- Feature engineering
- Feature serving
- Feature reuse
- Feast
12
MLOps Best Practices
- Documentation
- Security
- Cost optimization
- Team collaboration
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