Module: MLOps#
⭐⭐⭐⭐ | ⏱️ 8-10 hours
📊 Module Info#
Difficulty: ⭐⭐⭐⭐ Expert
Time Estimate: 8-10 hours
Prerequisites: All previous modules (01-13) - Complete TinyTorch ecosystem
Next Steps: 🎓 Course completion - Deploy your complete ML system!
Build production-ready ML systems with deployment, monitoring, and continuous learning. This capstone module integrates everything you’ve built into production-grade systems that can handle real-world challenges and scale to enterprise requirements.
🎯 Learning Objectives#
By the end of this module, you will be able to:
Design complete MLOps architectures: Orchestrate model development, deployment, and operations into production-ready systems
Implement model lifecycle management: Build versioning, registry, and deployment automation for reliable model operations
Create production serving systems: Deploy scalable, reliable model inference endpoints with monitoring and observability
Build continuous learning pipelines: Implement automated retraining, A/B testing, and model improvement workflows
Apply enterprise MLOps practices: Use industry-standard patterns for model governance, security, and compliance
🧠 Build → Use → Deploy#
This module follows TinyTorch’s Build → Use → Deploy framework:
Build: Implement complete MLOps infrastructure including model registry, serving, monitoring, and continuous learning systems
Use: Deploy and operate ML systems in production environments with real-world constraints and requirements
Deploy: Create end-to-end ML pipelines that demonstrate mastery of the entire TinyTorch ecosystem
📚 What You’ll Build#
Complete Model Lifecycle Management#
# Enterprise-grade model registry and versioning
from tinytorch.core.mlops import ModelRegistry, ModelMetadata
# Model registry with comprehensive metadata
registry = ModelRegistry("production")
metadata = ModelMetadata(
name="image_classifier_v2",
version="2.1.0",
training_data="cifar10_v3",
compression_applied=True,
performance_metrics={'accuracy': 0.94, 'latency_ms': 23},
compliance_approved=True
)
# Register model with full lifecycle tracking
model_id = registry.register_model(
model=optimized_model,
metadata=metadata,
artifacts=['weights.pt', 'config.json', 'benchmark_report.html']
)
# Model comparison and governance
comparison = registry.compare_models("2.0.0", "2.1.0")
deployment_approval = registry.approve_for_production(model_id)
Production Serving Infrastructure#
# Scalable model serving with monitoring
from tinytorch.core.mlops import ModelServer, LoadBalancer, HealthChecker
# Configure production server
server = ModelServer(
model_id=model_id,
max_concurrent_requests=100,
timeout_ms=500,
auto_scaling=True,
health_check_interval=30
)
# Load balancing across multiple instances
load_balancer = LoadBalancer(
servers=[server1, server2, server3],
strategy='round_robin',
health_aware=True
)
# Inference endpoint with comprehensive logging
@server.endpoint('/predict')
def predict(request):
start_time = time.time()
# Input validation and preprocessing
validated_input = validate_input(request.data)
preprocessed_input = preprocess(validated_input)
# Model inference
prediction = model.predict(preprocessed_input)
# Logging and monitoring
latency = (time.time() - start_time) * 1000
logger.log_prediction(request.id, prediction, latency)
monitor.track_inference(latency, prediction.confidence)
return jsonify({'prediction': prediction.tolist(), 'confidence': prediction.confidence})
Advanced Monitoring and Observability#
# Comprehensive production monitoring
from tinytorch.core.mlops import ModelMonitor, DriftDetector, AlertManager
# Multi-dimensional monitoring system
monitor = ModelMonitor(model_id)
monitor.track_performance_metrics(['latency', 'throughput', 'accuracy'])
monitor.track_business_metrics(['conversion_rate', 'user_satisfaction'])
monitor.track_infrastructure_metrics(['cpu_usage', 'memory_usage', 'error_rate'])
# Advanced drift detection
drift_detector = DriftDetector(
reference_dataset=training_data,
detection_methods=['statistical', 'adversarial', 'embedding_drift'],
alert_threshold=0.05
)
# Real-time alerting system
alert_manager = AlertManager()
alert_manager.configure_alerts({
'latency_p99_ms': {'threshold': 100, 'severity': 'critical'},
'accuracy_drop': {'threshold': 0.02, 'severity': 'high'},
'drift_score': {'threshold': 0.05, 'severity': 'medium'},
'error_rate': {'threshold': 0.01, 'severity': 'high'}
})
A/B Testing and Experimentation#
# Production-grade experimentation framework
from tinytorch.core.mlops import ExperimentManager, TrafficSplitter
# Configure A/B test
experiment = ExperimentManager("image_classifier_optimization")
experiment.add_variant("control", model_v2_0, traffic_percentage=70)
experiment.add_variant("treatment", model_v2_1, traffic_percentage=30)
# Statistical experiment design
experiment.configure_statistical_parameters(
significance_level=0.05,
minimum_detectable_effect=0.01,
power=0.8,
expected_runtime_days=14
)
# Traffic splitting with session consistency
traffic_splitter = TrafficSplitter(experiment)
@server.endpoint('/predict')
def predict_with_experiment(request):
# Determine experiment variant
variant = traffic_splitter.assign_variant(request.user_id)
model = experiment.get_model(variant)
# Make prediction and log experiment data
prediction = model.predict(request.data)
experiment.log_outcome(request.user_id, variant, prediction, request.ground_truth)
return prediction
# Automated experiment analysis
experiment_results = experiment.analyze_results()
if experiment_results.significant_improvement:
experiment.promote_winner()
Continuous Learning and Automation#
# Automated model improvement pipeline
from tinytorch.core.mlops import ContinuousLearner, AutoMLPipeline
# Continuous learning system
learner = ContinuousLearner(
base_model=current_production_model,
retraining_schedule='weekly',
data_freshness_threshold=7, # days
performance_threshold_drop=0.02
)
# Automated pipeline orchestration
pipeline = AutoMLPipeline()
pipeline.configure_stages([
'data_validation',
'feature_engineering',
'model_training',
'model_evaluation',
'compression_optimization',
'performance_validation',
'a_b_testing',
'production_deployment'
])
# Trigger automated improvement
@learner.schedule('weekly')
def automated_model_improvement():
# Collect new training data
new_data = data_collector.get_recent_data(days=7)
# Validate data quality
if data_validator.validate(new_data):
# Retrain model with new data
improved_model = pipeline.train_improved_model(
base_model=current_production_model,
additional_data=new_data
)
# Automated evaluation
if pipeline.meets_production_criteria(improved_model):
# Deploy to A/B test
experiment_manager.deploy_candidate(improved_model)
Enterprise Integration and Governance#
# Production ML system with enterprise features
from tinytorch.core.mlops import MLOpsPlatform, GovernanceEngine
# Complete MLOps platform
platform = MLOpsPlatform()
platform.configure_enterprise_features({
'model_governance': True,
'audit_logging': True,
'compliance_tracking': True,
'role_based_access': True,
'encryption_at_rest': True,
'encryption_in_transit': True
})
# Governance and compliance
governance = GovernanceEngine()
governance.configure_policies({
'model_approval_required': True,
'bias_testing_required': True,
'performance_monitoring_required': True,
'data_lineage_tracking': True,
'model_explainability_required': True
})
# Complete deployment with governance
deployment = platform.deploy_model(
model=approved_model,
environment='production',
governance_checks=governance.get_required_checks(),
monitoring_config=monitor.get_config(),
serving_config=server.get_config()
)
🚀 Getting Started#
Prerequisites#
Ensure you have completed the entire TinyTorch journey:
# Activate TinyTorch environment
source bin/activate-tinytorch.sh
# Verify complete ecosystem (this is the final capstone!)
tito test --module tensor # Foundation
tito test --module activations # Neural network components
tito test --module layers # Building blocks
tito test --module networks # Architectures
tito test --module cnn # Computer vision
tito test --module dataloader # Data engineering
tito test --module autograd # Automatic differentiation
tito test --module optimizers # Learning algorithms
tito test --module training # End-to-end training
tito test --module compression # Model optimization
tito test --module kernels # Performance optimization
tito test --module benchmarking # Evaluation methodology
Development Workflow#
Open the development file:
modules/source/14_mlops/mlops_dev.pyImplement model lifecycle management: Build registry, versioning, and metadata systems
Create production serving: Develop scalable inference endpoints with monitoring
Add monitoring and observability: Build comprehensive tracking and alerting systems
Build experimentation framework: Implement A/B testing and statistical validation
Create continuous learning: Develop automated improvement and deployment pipelines
Complete capstone project: Integrate entire TinyTorch ecosystem into production system
🧪 Testing Your Implementation#
Comprehensive Test Suite#
Run the full test suite to verify complete MLOps system functionality:
# TinyTorch CLI (recommended)
tito test --module mlops
# Direct pytest execution
python -m pytest tests/ -k mlops -v
Test Coverage Areas#
✅ Model Lifecycle Management: Verify registry, versioning, and metadata tracking
✅ Production Serving: Test scalable inference endpoints and load balancing
✅ Monitoring Systems: Ensure comprehensive tracking and alerting functionality
✅ A/B Testing Framework: Validate experimental design and statistical analysis
✅ Continuous Learning: Test automated retraining and deployment workflows
✅ Enterprise Integration: Verify governance, security, and compliance features
Inline Testing & Production Validation#
The module includes comprehensive MLOps validation and enterprise readiness verification:
# Example inline test output
🔬 Unit Test: Model lifecycle management...
✅ Model registry stores and retrieves models correctly
✅ Versioning system tracks model evolution
✅ Metadata management supports governance requirements
📈 Progress: Model Lifecycle ✓
# Production serving testing
🔬 Unit Test: Production inference endpoints...
✅ Server handles concurrent requests correctly
✅ Load balancing distributes traffic evenly
✅ Health checks detect and route around failures
📈 Progress: Production Serving ✓
# Monitoring and observability
🔬 Unit Test: Production monitoring systems...
✅ Performance metrics tracked accurately
✅ Drift detection identifies data changes
✅ Alert system triggers on threshold violations
📈 Progress: Monitoring & Observability ✓
# End-to-end integration
🔬 Unit Test: Complete MLOps pipeline...
✅ All TinyTorch components integrate successfully
✅ Production deployment meets enterprise requirements
✅ Continuous learning pipeline operates automatically
📈 Progress: Complete MLOps System ✓
Capstone Project Validation#
# Complete system integration test
from tinytorch.core.mlops import MLOpsPlatform
from tinytorch.core.training import Trainer
from tinytorch.core.compression import quantize_model
from tinytorch.core.kernels import optimize_inference
# End-to-end pipeline validation
platform = MLOpsPlatform()
# Train model using TinyTorch training system
trainer = Trainer(model, optimizer, loss_fn)
trained_model = trainer.fit(train_loader, val_loader, epochs=50)
# Optimize using compression and kernels
compressed_model = quantize_model(trained_model)
optimized_model = optimize_inference(compressed_model)
# Deploy to production with full MLOps
deployment = platform.deploy_complete_system(
model=optimized_model,
monitoring=True,
a_b_testing=True,
continuous_learning=True
)
print(f"✅ Complete TinyTorch system deployed successfully!")
print(f"📊 Model accuracy: {deployment.metrics['accuracy']:.4f}")
print(f"⚡ Inference latency: {deployment.metrics['latency_ms']:.2f}ms")
print(f"🚀 Production endpoint: {deployment.endpoint_url}")
🎯 Key Concepts#
Real-World Applications#
Netflix: Recommendation system deployment with A/B testing and continuous learning
Uber: Real-time demand prediction with monitoring and automated retraining
Spotify: Music recommendation MLOps with experimentation and personalization
Tesla: Autonomous driving model deployment with safety monitoring and over-the-air updates
MLOps Architecture Patterns#
Model Registry: Centralized model versioning, metadata, and artifact management
Serving Infrastructure: Scalable, reliable model inference with load balancing and health monitoring
Observability: Comprehensive monitoring of model performance, data quality, and system health
Experimentation: Statistical A/B testing for safe model deployment and improvement validation
Production ML Engineering#
Deployment Automation: CI/CD pipelines for model deployment with safety checks and rollback capabilities
Performance Optimization: Integration of compression, quantization, and hardware optimization
Reliability Engineering: Fault tolerance, disaster recovery, and high availability design
Security and Governance: Model security, audit trails, and compliance with regulations
Continuous Learning Systems#
Automated Retraining: Data-driven model improvement with performance monitoring
Feedback Loops: Online learning and adaptation based on production performance
Quality Assurance: Automated testing and validation before production deployment
Business Impact: Connecting ML improvements to business metrics and outcomes
🎉 Ready to Build?#
🎓 Congratulations! You’ve reached the capstone module of TinyTorch! This is where everything comes together—all the tensors, layers, networks, data loading, training, optimization, and evaluation you’ve built will be integrated into a production-ready ML system.
You’re about to build the same MLOps infrastructure that powers the AI systems you use every day. From recommendation engines to autonomous vehicles, they all depend on the deployment patterns, monitoring systems, and continuous learning pipelines you’re implementing.
Take your time, think about the big picture, and enjoy creating a complete ML system that’s ready for the real world. This is your moment to demonstrate mastery of the entire ML engineering stack! 🚀
Choose your preferred way to engage with this module:
Run this module interactively in your browser. No installation required!
Use Google Colab for GPU access and cloud compute power.
Browse the Python source code and understand the implementation.
💾 Save Your Progress
Binder sessions are temporary! Download your completed notebook when done, or switch to local development for persistent work.
Ready for serious development? → 🏗️ Local Setup Guide