MLOps Checklist for Deploying an AI Model in an SME

MLOps — Machine Learning Operations — is the practice of applying DevOps principles to the deployment and operation of machine learning models in production. For SMEs deploying AI models (whether fine-tuned custom models, RAG-based systems, or production AI integrations), MLOps practice is the difference between an AI deployment that works reliably and one that silently degrades, produces unpredictable results, or fails without detection. This checklist covers the MLOps essentials for SME AI deployments — practical, not theoretical, with emphasis on the checks that prevent the most common production failures.

Pre-Deployment Checklist

Model Validation

• Define evaluation metrics before deployment: what does “good performance” mean for this model? Accuracy, F1 score, BLEU score for generation tasks, or business metrics like lead qualification precision/recall? Establish minimum thresholds that the model must meet to proceed to production.
• Test against a held-out validation set: never evaluate on training data. Validation set should include edge cases and distribution shifts you anticipate in production.
• Adversarial testing: deliberately test with malformed inputs, adversarial prompts (for LLM-based systems), boundary cases, and the inputs most likely to produce incorrect outputs.
• Latency benchmarks: measure P50, P95, and P99 response times under expected production load. Define acceptable thresholds. P95 > 2 seconds for synchronous user-facing tasks is generally unacceptable.

Infrastructure Readiness

• Serve behind an API with versioning: expose the model via a versioned API endpoint (`/v1/model/predict`). This allows rolling updates without breaking integrations.
• Resource scaling: define compute requirements for expected load. Configure auto-scaling if on cloud infrastructure. Know at what request volume the current setup saturates.
• Fallback handling: what happens when the model is unavailable, times out, or returns an error? Define and implement the fallback behavior: return cached result, degrade gracefully with a rule-based alternative, or surface an explicit error with a retry option.
• Authentication and rate limiting: AI model endpoints require access control (API keys or service-to-service auth) and rate limiting to prevent abuse and uncontrolled cost escalation.

Monitoring Checklist (Post-Deployment)

Performance Monitoring

• Latency monitoring: track P50, P95, P99 latency in production. Alert when P95 exceeds threshold. Latency degradation is often the first signal of model or infrastructure issues.
• Error rate monitoring: track model errors (invalid outputs, null returns, exception rates). Alert when error rate exceeds 1% of requests.
• Throughput monitoring: requests per minute over time. Unusual spikes may indicate abuse; gradual increase helps predict scaling needs.

Model Quality Monitoring (Data Drift Detection)

• Input distribution monitoring: track the distribution of key input features over time. When the real-world distribution shifts significantly from the training distribution, model performance degrades — often invisibly. Alert on significant distribution shift.
• Output distribution monitoring: track the distribution of model outputs. If a classification model that historically predicted 60% class A suddenly shifts to 80% class A, something has changed — in the inputs, the model, or both.
• Human-labeled sample evaluation: periodically sample 50-100 predictions for human review against ground truth. This is the only reliable way to detect subtle quality degradation in production.

Retraining and Update Checklist

• Retrain trigger: define what triggers a model update — time-based (quarterly), performance-based (human eval score drops below threshold), or data-based (new training data accumulated).
• A/B test new versions: never replace production model without A/B testing new version against current version on live traffic with defined success criteria.
• Rollback procedure: maintain the previous production model version ready for immediate rollback if the new version underperforms. Test the rollback procedure before it’s needed.
• Model registry: maintain a model registry (even a simple spreadsheet) tracking model versions, training dates, datasets used, evaluation metrics, and deployment history. Essential for debugging and compliance.

Conclusion: MLOps for SME AI Deployments with Les Communicateurs

MLOps discipline is what separates AI deployments that work reliably in production from those that work in demos. For SMEs that have invested in building AI capabilities, protecting that investment through proper deployment practices, monitoring, and maintenance is not optional overhead — it’s what makes the investment durable.

Les Communicateurs advises on and implements MLOps practices for SME AI deployments, from pre-deployment validation through production monitoring and model update management. Contact us to discuss your AI deployment readiness.