rajesharigala/mlflow-pyfunc repository overview

⁠MLflow PyFunc Serving Image (mlflow-pyfunc:3.6.0)

This Docker image packages MLflow PyFunc model serving runtime for production-style inference deployments.

⁠What is this image?

This image is intended to serve MLflow PyFunc models as a REST API using MLflow's built-in model serving stack.
It is suitable for cloud deployments and local testing.

⁠Base Image

• Derived from AWS ECR MLflow PyFunc runtime
• Python-based runtime optimized for MLflow model serving

⁠MLflow Version

• MLflow PyFunc: 3.6.0 (tagged)
• Image tag is pinned to avoid breaking changes from latest

⁠Intended Usage

This image can be used for:

• AWS ECS / Fargate deployments
• AWS SageMaker custom inference containers
• Local Docker / Docker Compose testing
• Kubernetes-based ML inference services

Example (local run):

docker run -p 5001:5001 rajesharigala/mlflow-pyfunc:3.6.0

services:
  mlflow-serving:
    image: rajesharigala/mlflow-pyfunc:3.6.0
    ports:
      - "5001:5001"

Notes
This image is intended for model serving only, not training.
Always prefer versioned tags (3.6.0) in production instead of latest.
Compatible with MLflow models logged in PyFunc format.

Author
Maintained by Rajesh Arigala as part of production-grade MLOps / AI Platform projects.