MLOps Architecture: How to Build CI/CD for AI Models in Production (2026)

Q: What is MLOps and how is it different from DevOps?

MLOps extends DevOps practices to machine learning systems, but addresses challenges that traditional DevOps was not designed for. While DevOps manages deterministic code deployments, MLOps manages probabilistic model deployments where testing is statistical, artifacts include trained weights alongside code, data dependencies create implicit failure modes, and performance degradation is often silent. MLOps adds data versioning, feature stores, model evaluation gates, drift monitoring, and automated retraining loops to the standard CI/CD pipeline.

Q: What is the most common cause of ML model failures in production?

Training-serving skew — where the feature computation differs between training and production inference — is the single most prevalent cause of production ML failures. It occurs when features are calculated using different code paths, different data sources, or different library versions during training versus serving. The solution is a feature store that guarantees consistent feature computation across both environments.

Q: How long does it take to build an MLOps platform?

Building on open-source components typically takes six to nine months with a team of three to five MLOps engineers. Using managed cloud platforms like AWS SageMaker, Google Vertex AI, or Azure ML reduces this to two to four months, though with increased vendor lock-in and per-unit costs. Start with the minimum viable platform — experiment tracking, a basic training pipeline, and model monitoring — and expand incrementally.

Q: What MLOps maturity level should my organization target?

Match your MLOps maturity to your model portfolio. One to three production models: Level 1 with automated training and manual deployment. Five to fifteen models: Level 2 with automated deployment and manual monitoring. Twenty or more models: Level 3 with closed-loop automation. Jumping directly to Level 3 without operational experience at each prior level is the most common and most expensive mistake.

Q: How much does MLOps infrastructure cost?

Platform infrastructure costs $200K-$500K annually for a mid-maturity setup including compute, storage, and tooling. GPU serving costs dominate — a single GPU instance can cost $25K-$100K per year. The key cost optimization strategies are model quantization, dynamic batching, auto-scaling, and model routing that sends simple requests to cheaper models. Track cost per prediction and set per-model budgets.

Q: Should I build my MLOps platform or buy a managed solution?

Most enterprises benefit from a hybrid approach: managed services for operationally heavy components like feature serving, model hosting, and auto-scaling, combined with open-source tools for experiment tracking, pipeline definitions, and monitoring where flexibility matters. Pure build requires strong infrastructure teams. Pure buy creates deep vendor lock-in that is expensive to reverse.

Q: How do I monitor for model degradation if ground truth labels are delayed?

Use a layered monitoring approach. Data drift monitoring detects input distribution changes immediately. Prediction drift monitoring tracks output distribution changes without needing labels. Performance monitoring against ground truth runs on whatever delay your domain requires — hours for click-through rates, months for credit defaults. Together, these three layers provide early warning even when direct performance measurement is delayed.

Q: What governance requirements does the EU AI Act impose on MLOps?

The EU AI Act requires high-risk AI systems to maintain documented model lineage from training data through deployment, bias and fairness auditing before and during production, technical documentation describing model capabilities and limitations, and ongoing performance monitoring with human oversight mechanisms. The MLOps platform must enforce these requirements programmatically through automated pipeline gates and continuous monitoring.