CAM Infrastructure Assessment Platform - MLOps Reference Architecture

Following Google Cloud MLOps Continuous Delivery Pattern | Level 2: CI/CD Pipeline Automation
1
2
3
4
5
6
📊
Model Analysis
XGBoost: 0.89 F1
YOLO: mAP 0.87
RF: R²=0.83
Physics: 6% RMSE
🧪
Orchestrated Exp.
Vertex AI Pipeline
Hyperparameter
tuning
Isaac Sim valid.
47K samples
💻
Source Code
Python
TensorFlow
scikit-learn
YOLOv8
XGBoost
📦
Source Repo
GitHub
Version control
Model configs
Pipeline code
🔧
CI: Build, Test & Package
Cloud Build | TFLite conversion
INT8 quantization | Unit tests
Integration tests | Validation
📦
Packages
Model artifacts
4.2MB TFLite
Docker images
Configs
💾
Feature Store
BigQuery
280GB storage
2.1M segments
470K POIs
EPSG:27700
S2 indexing
85ms queries

Features
17-dim POI
12-dim road
Spatial 50m
Temporal

Cache
85% hit rate
L1/L2/L3
🗺️
OSM: 2.1M seg, 470K POIs
🌐
Google Maps + Street View
🎮
Isaac Sim Physics
📊 Automated ML Pipeline
📥
Data Extract
OSM 2.1M seg
Street View
640×640
Fleet 500hr
WGS84↔UTM
Polygons
✅
Data Valid.
Schema
check
Quality
18% err
Profiling
κ=0.82
🛠️
Data Prep
80/20 split
YOLO
augment
Normalize
Feature
engineer
🤖
Model Train
XGBoost
500 trees
YOLOv8x
18 hours
RF 200T
Physics NN
📊
Model Eval.
F1 score
mAP@0.5
R²
RMSE
Confusion
P/R curve
✔️
Model Valid.
Isaac Sim
50 routes
Crash
ρ=0.71
Expert
κ=0.79
📚
Model Registry
Vertex AI
Versioning
Metadata
Lineage
tracking
A/B tests
🎓
Trained Model
Knowledge
Distillation
MobileNetV3
0.86 F1
12ms
4.2MB
🚀
CD: Pipeline Deploy
Cloud Build
Feature flags
A/B test
Gradual rollout
📲
CD: Model Serving
On-Device
(70%)
TFLite
12ms
0.86 F1

Edge (18%)
GPU 35ms
Physics NN

Cloud (5%)
YOLO 312ms
High-stakes
🗄️
ML Metadata Store
Experiment tracking | Model lineage | Feature provenance | Training metrics | Validation results | Deployment history
🔔
Trigger
Drift detection
Performance
drop
Scheduled
retrain
Volume
threshold
Manual
override
📈
Performance Monitoring
Latency
133ms p50
F1 drift
tracking
Data quality
checks
Business
80% completion
NPS 68 (+8)
🎯
Prediction Service
12K active
users
145K routes
32% DAU/MAU
0 incidents
(6mo pilot)
£1.8M infra
decisions
$0.35/user/mo
Components
Data Sources
Processing
ML Models
Storage
Deployment
Monitoring