model-deployment

Deploying machine learning models to production with Docker and Kubernetes involves packaging your trained model into a container using Dockerfile examples and defining Kubernetes manifests for orchestration to ensure scalable and reliable inference endpoints.

Serving real-time ML inference endpoints is best achieved using dedicated serving frameworks like TorchServe, TensorFlow Serving, or Triton Inference Server, combined with FastAPI to handle fluctuating traffic through Kubernetes autoscaling.

Yes, you can use MLflow for model versioning by registering models, managing staging environments, and executing canary deployments to safely transition machine learning models into production.

Containerization for batch and streaming inference works by packaging the model and its dependencies into a Docker container, allowing Kubernetes to orchestrate scalable execution across different deployment strategies.

You need TorchServe to package and serve PyTorch models specifically, whereas TensorFlow Serving is designed for TensorFlow models, and Triton Inference Server supports multiple frameworks for production deployment.

Using Kubernetes manifests for ML model deployment ensures your inference endpoints are scalable, reliable, and maintainable by automating container orchestration, traffic management, and resource allocation across production environments.