ai-architect-expert

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}