MLOps Best Practices for 2026

Modern MLOps blends software reliability with data and model governance. Here’s a concise playbook for 2026.

1. Version Everything

Datasets, features, models, prompts, and configs. Ensure reproducibility and traceability across the lifecycle.

2. CI/CD for ML

• Training pipelines run reproducibly with environment pinning
• Automated eval gates deploys; canaries + shadow traffic for safety

3. Production Observability

Track latency, throughput, accuracy, drift, and safety incidents. Use tracing for feature fetch → inference → post‑processing.

4. Responsible AI

Consentful data practices, redaction, audit trails, and policy checks. Guardrails for LLMs.

5. Cost Controls

Batching, quantization, caching, and right‑sizing hardware. Measure cost by tenant/model/version.

6. Team Practices

Define SLOs for ML endpoints, rollback strategies, and incident response. Treat models like services.