juniorMLOps
What is containerization in ML deployment?
Updated May 17, 2026
Short answer
Containerization packages ML models and dependencies into isolated environments.
Deep explanation
Docker ensures portability across environments, avoiding dependency issues.
Real-world example
Deploying a sentiment analysis API using Docker containers.
Common mistakes
- Ignoring image size optimization.
Follow-up questions
- Why use Docker?
- What is Kubernetes role?