Deployment

Containerization (AI)

Definition

Packaging AI models and their dependencies into portable, isolated containers (Docker) for consistent deployment across environments. Containerization eliminates "works on my machine" problems and is the foundation of reproducible, auditable AI production systems.

Related Terms

Kubernetes for ML

Using the Kubernetes container orchestration platform to manage scalable, fault-tolerant AI/ML workloads in production. Kubernetes enables auto-scaling inference services, GPU resource management, and rolling model updates with minimal downtime.

MLOps

The practice of combining Machine Learning, DevOps, and data engineering to streamline the deployment, monitoring, and maintenance of ML models in production. MLOps ensures reliable, scalable, and reproducible ML systems across their entire lifecycle.

Model Serving

The process of deploying trained ML models to production environments where they can receive inputs and return predictions at scale. Model serving infrastructure must address throughput, latency, versioning, and cost while meeting SLAs.

Knowing the Terms Is Step One. Applying Them Is Step Two.

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