MLOps and Model Lifecycle¶

📖 NVIDIA AI Enterprise - Enterprise AI platform documentation

ML Pipeline Architecture¶

Pipeline Stages¶

Data Management: - Data ingestion from multiple sources - Data validation and quality checks - Feature engineering and transformation - Feature store for consistent serving - Data versioning for reproducibility

Model Development: - Experiment tracking (hyperparameters, metrics, artifacts) - Hyperparameter tuning (grid search, Bayesian optimization) - Distributed training on GPU clusters - Model evaluation against baseline metrics - Reproducibility through version control of code, data, and config

Model Deployment: - Model packaging (containerization, model formats) - Deployment to staging for validation - Production deployment with traffic management - Monitoring for performance degradation - Automated rollback on metric decline

Pipeline Orchestration¶

Kubeflow Pipelines: - Kubernetes-native ML workflow orchestration - DAG-based pipeline definition - Reusable components - Integration with GPU scheduling - Experiment tracking and comparison

Apache Airflow: - General-purpose DAG orchestration - Rich operator ecosystem - Scheduling and retry policies - Integration with cloud services - Custom operators for GPU workloads

NVIDIA Base Command: - Purpose-built for GPU workload management - Integrated job scheduling and monitoring - Container-native job submission - Dataset management - GPU performance profiling

📖 Base Command Documentation - Job management

Model Versioning and Registry¶

Model Artifacts¶

What to Version: - Trained model weights (checkpoint files) - Model architecture definition - Training configuration (hyperparameters, optimizer settings) - Preprocessing and postprocessing code - Training data version reference - Evaluation metrics and validation results - Environment specification (dependencies, GPU requirements)

Model Registry¶

MLflow Model Registry: - Central repository for model artifacts - Stage management: None, Staging, Production, Archived - Transition approval workflows - Metadata and tag management - REST API for programmatic access

NGC (NVIDIA GPU Cloud): - NVIDIA's model and container registry - Pre-trained models and fine-tuned variants - Optimized containers for training and inference - Model cards with performance benchmarks - Version management for NVIDIA models

📖 NGC Catalog - NVIDIA model and container registry

Lineage Tracking¶

• Track data sources used for training
• Record code version (git commit hash)
• Log hardware and software environment
• Link evaluation metrics to specific model versions
• Enable full reproducibility of any model version

CI/CD for Machine Learning¶

ML-Specific CI/CD¶

Continuous Integration: - Automated unit tests for data processing code - Model training sanity checks on small datasets - Code quality and linting checks - Container build and vulnerability scanning - Feature validation tests

Continuous Delivery: - Automated model training on new data - Model validation against performance thresholds - Deployment to staging environment - Integration testing with downstream services - Manual or automated promotion to production

Continuous Training: - Triggered by data drift detection - Triggered by scheduled retraining cadence - Triggered by new labeled data availability - Automated evaluation and comparison to current model - Conditional promotion based on performance improvement

Validation Gates¶

Model Quality Gates: - Accuracy/F1 above minimum threshold - Latency within SLA requirements - Model size within deployment constraints - Fairness and bias metrics acceptable - No regression on critical test cases

Infrastructure Gates: - Container builds successfully - Passes security scanning - GPU resource requirements verified - Inference endpoint responds correctly - Health check endpoints functional

Experiment Tracking¶

Key Metrics to Track¶

Training Metrics: - Loss curves (training and validation) - Learning rate schedule - GPU utilization during training - Training throughput (samples/second) - Time to convergence

Model Quality Metrics: - Accuracy, precision, recall, F1 - Latency (P50, P95, P99) - Throughput (inferences/second) - Model size and memory footprint - Calibration quality

Experiment Comparison¶

• Compare metrics across experiment runs
• Visualize hyperparameter impact
• Identify best configurations
• Track experiment lineage
• Share results across team members

Key Exam Concepts¶

• ML pipeline stages and orchestration tools
• Model versioning, registry, and artifact management
• CI/CD practices specific to ML workflows
• Validation gates for model promotion
• Experiment tracking and reproducibility
• Lineage tracking for full traceability