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Background

MLOps & LLMOps for Production AI

Monitor, version, and optimize LLM applications in production with MLflow, Weights & Biases, LangSmith, and custom observability pipelines.

MLOps & LLMOps Capabilities

Model Monitoring & Observability

Track model performance, token usage, latency, and quality metrics in production with real-time alerting for regressions and anomalies.

Experiment Tracking & Versioning

Version prompts, model configurations, and evaluation datasets with reproducible experiment tracking for A/B testing and rollback.

Drift Detection & Quality Assurance

Monitor for prompt drift, embedding drift, and output quality degradation with automated retraining triggers and regression testing.

Cost Optimization & Resource Management

Track API costs, optimize token usage, cache responses, and implement request throttling to control LLM infrastructure spend.

Our Three-Layer Approach to MLOps

1

Advisory & Governance

MLOps strategy, tooling selection, and monitoring framework design for LLM applications.

  • • MLOps platform selection (MLflow, W&B, LangSmith, custom)
  • • KPI definition for LLM quality (accuracy, relevance, safety)
  • • Model governance policies and approval workflows
  • • Prompt versioning and change management strategy
  • • Cost monitoring and budget alert configuration

Example Deliverable:

LLMOps governance framework with monitoring KPIs and alerting policies

2

Build & Integrate

Production-grade observability, logging, and evaluation pipelines for LLM applications.

  • • LangSmith tracing integration for agent debugging
  • • MLflow experiment tracking for prompt A/B tests
  • • Custom metrics dashboards (Grafana, Datadog, CloudWatch)
  • • Automated evaluation pipelines with golden datasets
  • • Cost tracking per user, endpoint, or business unit

Example Deliverable:

LangSmith observability stack with custom quality metrics and cost dashboards

3

Operate & Scale

Continuous improvement, automated retraining, and production incident response for LLM systems.

  • • Real-time alerting for quality regressions and cost spikes
  • • Automated prompt optimization based on production feedback
  • • Shadow deployments and canary releases for new prompts
  • • Incident response playbooks for model failures
  • • Periodic retraining with fresh production data

Example Deliverable:

Automated monitoring with PagerDuty alerts and incident response runbooks

Real-World MLOps Implementations

Voice Agent Quality Monitoring

Custom observability stack tracking interview completion rate, candidate sentiment scores, and LLM token costs per call with real-time Slack alerts for quality issues.

Tech:Custom Metrics, Grafana, PostgreSQL Time-Series

Healthcare Outreach Campaign Optimization

A/B testing infrastructure for appointment confirmation prompts with MLflow tracking of confirmation rates, call duration, and escalation frequency across prompt variants.

Tech:MLflow, Python, Statistical Testing

Field Service Knowledge Base Monitoring

LangSmith integration for tracking retrieval precision, answer relevance, and technician feedback scores with automated reindexing when search quality degrades.

Tech:LangSmith, LlamaIndex, Custom Evaluation

Key Metrics We Track

Quality Metrics

  • • Response accuracy & relevance
  • • Hallucination detection rate
  • • User satisfaction scores
  • • Semantic similarity to ground truth
  • • Safety violations & toxicity

Performance Metrics

  • • End-to-end latency (p50, p95, p99)
  • • Token throughput per second
  • • Cache hit rate
  • • API error rate & retries
  • • Concurrent request handling

Business Metrics

  • • Cost per request/user/session
  • • Task completion rate
  • • Escalation to human rate
  • • ROI vs baseline automation
  • • User adoption & retention

LLMOps vs Traditional MLOps

LLM applications require unique monitoring and governance approaches:

Traditional MLOps

  • • Model retraining on schedule
  • • Structured input/output validation
  • • Prediction accuracy & F1 scores
  • • Feature drift monitoring
  • • Batch inference pipelines

LLMOps

  • • Prompt versioning & A/B testing
  • • Unstructured text quality evaluation
  • • Relevance, safety, & hallucination scoring
  • • Embedding drift & semantic shift
  • • Real-time streaming & agent workflows

Ready to operationalize your LLM applications?

Let's build monitoring, evaluation, and governance systems to ensure your AI performs reliably in production.

Schedule Consultation