A March 2026 AWS study found that MLOps CI/CD pipelines cut ML production failures by 92% through automated drift detection. Ninety-two percent. That is not a marginal improvement — that is the difference between a system that survives contact with the real world and one that quietly collapses. And yet most teams still treat deployment as a manual hand-off: a data scientist exports a model file, an engineer figures out how to serve it, and nobody owns what happens next. That gap has a name. It is called the MLOps deficit, and it is where production AI goes to die. In this lecture, we focus on the practical implementation of MLOps, emphasizing its role in ensuring model reliability and security through structured CI/CD pipelines. It unifies development with operations — Dev with Ops — applying automation, CI/CD, source control, and infrastructure as code directly to machine learning workflows. Traditional DevOps is insufficient here because ML systems have three moving parts instead of one: code, data, and models. All three must be versioned, tested, and deployed together. Google's MLOps architecture makes this concrete with six sequential stages. Stage one: development and experimentation, producing orchestrated source code. Stage two: pipeline CI, which builds that code and runs tests — not just unit tests, Yuan, but data schema validation, distribution shift checks, and model performance gates. Stage three: pipeline CD, deploying those artifacts to target environments. Stage four: automated triggering, executing pipelines on schedule or on data-drift signal, outputting trained models to a registry. Stage five: model CD, serving those models as live prediction services. Stage six: monitoring, collecting performance statistics and triggering the next retraining cycle. Six stages. Each one automated. Each one a checkpoint. Key MLOps components include a feature store, a model registry, an ML metadata store, and a pipeline orchestrator. These tools are essential for setting up robust CI/CD pipelines that ensure model reliability and security. Databricks released Unity Catalog in February 2026, enabling federated CI/CD across multi-cloud ML environments with zero-copy data sharing. Azure AKS introduced serverless CI/CD for edge AI models in March 2026, delivering ten times faster inference at scale. As of that same month, 78% of Fortune 500 companies had adopted Git-based MLOps CI/CD, cutting model deployment time by 65% on average. The tooling has matured fast, Yuan — the barrier now is organizational will, not technical availability. Versioning is where teams most often cut corners, and it is where they pay the highest price. Versioning code is familiar. Versioning models means tracking weights, hyperparameters, and training configurations in a registry so any deployment can be rolled back precisely. Versioning data is harder and more consequential — it means snapshotting the exact dataset state used for each training run, because a model and its data are inseparable artifacts. BigStepTech's 2026 report found that MLOps with predictive CI/CD triggers pre-empts 85% of model degradation events before they reach users. That number is only achievable when data versioning and monitoring are treated as first-class engineering concerns, not afterthoughts. Google Cloud's MLOps 2.5 update in January 2026 went further, integrating quantum-safe encryption directly into CI/CD pipelines — a signal that production ML infrastructure is now a security-critical system, not just an operational one. Reliable AI systems depend on automated CI/CD pipelines for retraining, testing, and deployment. MLOps addresses the bottleneck of manual hand-offs by automating transitions, ensuring consistency and reducing failure modes. It introduces lag, inconsistency, and invisible failure modes. MLOps eliminates that bottleneck by treating every ML asset — data, model, pipeline — exactly as software engineering treats code: versioned, tested, continuously integrated, continuously delivered. Build the pipeline first, Yuan. The model inside it is replaceable. The pipeline that keeps it honest is not.