Containerizing ML Models with Docker and Kubernetes

Deploying machine learning (ML) models as scalable microservices is a critical step in operationalizing ML workflows. By containerizing ML models with Docker and orchestrating them with Kubernetes, you can achieve scalability, reliability, and portability. This guide provides a comprehensive overview of how to containerize ML models, deploy them as microservices, and manage them using Kubernetes. …

Mar 13, 2025 - 08:36

Deploying machine learning (ML) models as scalable microservices is a critical step in operationalizing ML workflows. By containerizing ML models with Docker and orchestrating them with Kubernetes, you can achieve scalability, reliability, and portability. This guide provides a comprehensive overview of how to containerize ML models, deploy them as microservices, and manage them using Kubernetes. …