LLMOps: Operationalizing Large Language Models

LLMOps extends MLOps for the unique challenges of managing LLM applications in production.

LLMOps Framework

from dataclasses import dataclass
from typing import Dict, List
import mlflow
from datetime import datetime

@dataclass
class LLMDeployment:
    model_id: str
    version: str
    endpoint: str
    config: Dict

class LLMOpsManager:
    def __init__(self):
        self.deployments = {}
        self.metrics_store = MetricsStore()
        self.prompt_registry = PromptRegistry()

    async def deploy_model(self, config: LLMDeployment) -> str:
        """Deploy LLM with full observability."""
        # Register deployment
        deployment_id = f"{config.model_id}-{config.version}-{datetime.now().isoformat()}"

        # Set up monitoring
        self.setup_monitoring(deployment_id, config)

        # Configure scaling
        self.configure_autoscaling(deployment_id, config)

        # Set up A/B testing if needed
        if config.config.get("ab_test"):
            self.setup_ab_test(deployment_id, config)

        self.deployments[deployment_id] = config
        return deployment_id

    def setup_monitoring(self, deployment_id: str, config: LLMDeployment):
        """Configure comprehensive LLM monitoring."""
        monitors = [
            LatencyMonitor(threshold_ms=1000),
            QualityMonitor(min_score=0.7),
            CostMonitor(budget_per_day=100),
            ToxicityMonitor(threshold=0.1),
            DriftMonitor(baseline=config.config.get("baseline"))
        ]

        for monitor in monitors:
            self.metrics_store.register_monitor(deployment_id, monitor)

    async def track_interaction(self, deployment_id: str, interaction: Dict):
        """Track LLM interaction for analysis."""
        metrics = {
            "timestamp": datetime.now(),
            "deployment_id": deployment_id,
            "input_tokens": interaction["input_tokens"],
            "output_tokens": interaction["output_tokens"],
            "latency_ms": interaction["latency_ms"],
            "cost_usd": self.calculate_cost(interaction),
            "quality_score": await self.evaluate_quality(interaction),
            "user_feedback": interaction.get("feedback")
        }

        self.metrics_store.record(metrics)

        # Check for alerts
        await self.check_alerts(deployment_id, metrics)

    async def evaluate_quality(self, interaction: Dict) -> float:
        """Evaluate response quality."""
        evaluator = QualityEvaluator()
        return await evaluator.score(
            question=interaction["prompt"],
            response=interaction["response"],
            context=interaction.get("context")
        )

    def calculate_cost(self, interaction: Dict) -> float:
        """Calculate interaction cost."""
        pricing = {
            "gpt-4o": {"input": 0.005, "output": 0.015},
            "gpt-4o-mini": {"input": 0.00015, "output": 0.0006}
        }

        model = interaction["model"]
        if model not in pricing:
            return 0

        return (
            interaction["input_tokens"] / 1000 * pricing[model]["input"] +
            interaction["output_tokens"] / 1000 * pricing[model]["output"]
        )

    async def rollback(self, deployment_id: str, reason: str):
        """Rollback to previous version."""
        current = self.deployments[deployment_id]
        previous = self.get_previous_version(current.model_id)

        if previous:
            await self.deploy_model(previous)
            self.log_rollback(deployment_id, reason)

LLMOps ensures reliable, cost-effective LLM operations at scale.