Build expertise in managing and deploying machine learning workflows
Data Preparation
Data cleaning, transformation, feature engineering
Model Training
Training pipelines, hyperparameter tuning
Model Evaluation
Metrics, validation techniques, performance comparison
Containerization
Docker, orchestration, containerized workflows
Cloud Platforms
Deploying and managing ML workflows on cloud
Resource Scaling
Autoscaling, distributed training, infrastructure monitoring
CI/CD Pipelines
Automating testing, deployment, and monitoring
Model Versioning
Tracking model changes and reproducibility
Deployment Strategies
Batch, online, and edge deployments
Model Drift Detection
Monitoring changes in data and model performance
System Monitoring
Tracking infrastructure and application health
Cost Optimization
Efficient resource usage, cost analysis
Track your progress in MLOps skills and best practices
Master the art of deploying and maintaining ML systems