tomevault-io AI Architect Expert
Expert guidance for designing AI systems, MLOps architecture, scalable ML infrastructure, and AI platform engineering.
Core Concepts
AI System Architecture
- Model serving architectures
- Real-time vs batch inference
- Feature stores
- Model registries
- Training pipelines
- Data versioning
MLOps Infrastructure
- CI/CD for ML
- Model monitoring and observability
- A/B testing frameworks
- Model retraining automation
- Resource orchestration
- Cost optimization
Scalability Patterns
- Distributed training
- Model parallelism
- Data parallelism
- Inference optimization
- Caching strategies
- Load balancing
ML Platform Architecture
from dataclasses import dataclass
from typing import Dict, List, Optional
from enum import Enum
class ModelStage(Enum):
DEVELOPMENT = "development"
STAGING = "staging"
PRODUCTION = "production"
ARCHIVED = "archived"
@dataclass
class ModelMetadata:
name: str
version: str
framework: str
stage: ModelStage
metrics: Dict[str, float]
created_at: str
updated_at: str
class ModelRegistry:
"""Central model registry for ML platform"""
def __init__(self):
self.models: Dict[str, List[ModelMetadata]] = {}
def register_model(self, model: ModelMetadata) -> str:
"""Register new model version"""
if model.name not in self.models:
self.models[model.name] = []
self.models[model.name].append(model)
return f"{model.name}:{model.version}"
def promote_model(self, name: str, version: str, stage: ModelStage):
"""Promote model to different stage"""
for model in self.models.get(name, []):
if model.version == version:
model.stage = stage
return True
return False
def get_production_model(self, name: str) -> Optional[ModelMetadata]:
"""Get current production model"""
for model in self.models.get(name, []):
if model.stage == ModelStage.PRODUCTION:
return model
return None
class FeatureStore:
"""Feature store for ML features"""
def __init__(self):
self.features: Dict[str, Dict] = {}
self.feature_groups: Dict[str, List[str]] = {}
def register_feature(self, name: str, dtype: str, description: str,
transformation: Optional[str] = None):
"""Register feature definition"""
self.features[name] = {
"dtype": dtype,
"description": description,
"transformation": transformation
}
def create_feature_group(self, group_name: str, feature_names: List[str]):
"""Create feature group for reuse"""
self.feature_groups[group_name] = feature_names
def get_features(self, entity_id: str, feature_names: List[str]) -> Dict:
"""Retrieve feature values for entity"""
# In production, this would query online/offline stores
return {name: self._fetch_feature(entity_id, name)
for name in feature_names}
Training Pipeline Architecture
from abc import ABC, abstractmethod
import torch.distributed as dist
class TrainingPipeline(ABC):
"""Base training pipeline"""
def __init__(self, config: Dict):
self.config = config
self.experiment_tracker = None
self.checkpointer = None
@abstractmethod
def prepare_data(self):
"""Data preparation step"""
pass
@abstractmethod
def train(self):
"""Training step"""
pass
@abstractmethod
def evaluate(self):
"""Evaluation step"""
pass
def run(self):
"""Execute full pipeline"""
self.prepare_data()
self.train()
metrics = self.evaluate()
self.log_metrics(metrics)
return metrics
class DistributedTrainingPipeline(TrainingPipeline):
"""Distributed training with DDP"""
def __init__(self, config: Dict, world_size: int, rank: int):
super().__init__(config)
self.world_size = world_size
self.rank = rank
self.setup_distributed()
def setup_distributed(self):
"""Initialize distributed training"""
dist.init_process_group(
backend='nccl',
world_size=self.world_size,
rank=self.rank
)
def prepare_data(self):
"""Distribute data across workers"""
from torch.utils.data.distributed import DistributedSampler
self.sampler = DistributedSampler(
self.dataset,
num_replicas=self.world_size,
rank=self.rank
)
def train(self):
"""Distributed training loop"""
from torch.nn.parallel import DistributedDataParallel as DDP
model = DDP(self.model, device_ids=[self.rank])
for epoch in range(self.config['epochs']):
self.sampler.set_epoch(epoch)
for batch in self.dataloader:
loss = self.train_step(model, batch)
if self.rank == 0:
self.log_loss(loss)
Model Serving Architecture
from fastapi import FastAPI, BackgroundTasks
from prometheus_client import Counter, Histogram
import asyncio
# Metrics
prediction_counter = Counter('predictions_total', 'Total predictions')
prediction_latency = Histogram('prediction_latency_seconds', 'Prediction latency')
class ModelServer:
"""Production model serving"""
def __init__(self, model_registry: ModelRegistry):
self.registry = model_registry
self.loaded_models = {}
self.prediction_cache = {}
async def load_model(self, name: str, version: str = "production"):
"""Load model into memory"""
if version == "production":
model_metadata = self.registry.get_production_model(name)
else:
model_metadata = self.registry.get_model(name, version)
if not model_metadata:
raise ValueError(f"Model {name}:{version} not found")
# Load model from storage
model = await self._load_from_storage(model_metadata)
self.loaded_models[f"{name}:{version}"] = model
return model
@prediction_latency.time()
async def predict(self, model_name: str, features: Dict) -> Dict:
"""Make prediction with caching"""
prediction_counter.inc()
# Check cache
cache_key = self._generate_cache_key(model_name, features)
if cache_key in self.prediction_cache:
return self.prediction_cache[cache_key]
# Get model
model = self.loaded_models.get(model_name)
if not model:
model = await self.load_model(model_name)
# Predict
result = await self._run_inference(model, features)
# Cache result
self.prediction_cache[cache_key] = result
return result
async def predict_batch(self, model_name: str,
batch_features: List[Dict]) -> List[Dict]:
"""Batch prediction for efficiency"""
tasks = [self.predict(model_name, features)
for features in batch_features]
return await asyncio.gather(*tasks)
app = FastAPI()
model_server = ModelServer(model_registry=ModelRegistry())
@app.post("/predict/{model_name}")
async def predict_endpoint(model_name: str, features: Dict):
return await model_server.predict(model_name, features)
Monitoring and Observability
from dataclasses import dataclass
from datetime import datetime
import numpy as np
@dataclass
class PredictionLog:
timestamp: datetime
model_name: str
model_version: str
features: Dict
prediction: any
latency_ms: float
input_hash: str
class ModelMonitor:
"""Monitor model performance in production"""
def __init__(self):
self.logs: List[PredictionLog] = []
self.metrics = {}
def log_prediction(self, log: PredictionLog):
"""Log prediction for monitoring"""
self.logs.append(log)
# Update metrics
self.update_latency_metrics(log)
self.check_data_drift(log)
def update_latency_metrics(self, log: PredictionLog):
"""Track prediction latency"""
model_key = f"{log.model_name}:{log.model_version}"
if model_key not in self.metrics:
self.metrics[model_key] = {
"latencies": [],
"predictions": 0
}
self.metrics[model_key]["latencies"].append(log.latency_ms)
self.metrics[model_key]["predictions"] += 1
def check_data_drift(self, log: PredictionLog):
"""Detect data drift in input features"""
# Compare current feature distributions with training data
# Alert if significant drift detected
pass
def get_model_health(self, model_name: str) -> Dict:
"""Get model health metrics"""
model_metrics = self.metrics.get(model_name, {})
latencies = model_metrics.get("latencies", [])
return {
"total_predictions": model_metrics.get("predictions", 0),
"avg_latency_ms": np.mean(latencies) if latencies else 0,
"p95_latency_ms": np.percentile(latencies, 95) if latencies else 0,
"p99_latency_ms": np.percentile(latencies, 99) if latencies else 0
}
Best Practices
Architecture Design
- Separate training and serving infrastructure
- Use feature stores for consistency
- Implement model versioning from day one
- Design for horizontal scalability
- Plan for model rollback capability
- Build monitoring into the architecture
MLOps
- Automate model retraining pipelines
- Implement CI/CD for models
- Version everything (data, code, models)
- Monitor model performance metrics
- Track data drift and model decay
- Implement gradual rollout (canary/blue-green)
Infrastructure
- Use GPU efficiently (batching, mixed precision)
- Implement caching for repeated predictions
- Consider model compression (quantization, pruning)
- Plan for disaster recovery
- Optimize costs (spot instances, autoscaling)
- Use managed services where appropriate
Anti-Patterns
❌ No model versioning or registry ❌ Training and serving environment mismatch ❌ No monitoring or alerting ❌ Manual model deployment process ❌ Ignoring data drift ❌ No rollback strategy ❌ Over-engineering for initial MVP
Resources
- MLflow: https://mlflow.org/
- Kubeflow: https://www.kubeflow.org/
- Seldon Core: https://www.seldon.io/
- BentoML: https://www.bentoml.com/
- Weights & Biases: https://wandb.ai/
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