MLOps Pipeline
End-to-end machine learning lifecycle management from data ingestion to model deployment and monitoring.
High ComplexityTechnologies & Tools
MLflowKubeflowAirflowDVCWeights & BiasesSeldon Core
Architecture Flow
1
Data Ingestion
Collect and validate training data
Data PipelinesData ValidationFeature Engineering
2
Experiment Tracking
Track model experiments and hyperparameters
MLflowWeights & BiasesDVC
3
Model Training
Train models with versioned data and code
Distributed TrainingHyperparameter TuningModel Registry
4
Model Deployment
Deploy models to production with monitoring
Seldon CoreKubernetesModel Serving
Use Cases
Production ML systems
Large-scale model training
Team collaboration
Model versioning
Continuous deployment
Pros
Reproducible experiments
Automated workflows
Team collaboration
Model versioning
Production deployment
Cons
High complexity
Tooling costs
Operational overhead
Learning curve
Infrastructure management
When to Use
Production ML systems
Team collaboration
Model versioning needs
Automated workflows
Large-scale training
Alternatives
Manual workflowsSimple scriptsCloud ML platformsCustom solutions
Performance Metrics
Latency
Low (minutes to hours for training)
Throughput
High (parallel training)
Scalability
Excellent
Reliability
High
Cost
High
Key Trade-offs
Complexity
High operational complexity and tooling
Reproducibility
Excellent experiment tracking and reproducibility
Cost
Infrastructure and tooling costs
Category Information
Category
MLOps
Complexity Level
High