Production ML Serving Boilerplate: Skip the Infrastructure Setup
Published:Dec 29, 2025 07:39
•1 min read
•r/mlops
Analysis
This article introduces a production-ready ML serving boilerplate designed to streamline the deployment process. It addresses a common pain point for MLOps engineers: repeatedly setting up the same infrastructure stack. By providing a pre-configured stack including MLflow, FastAPI, PostgreSQL, Redis, MinIO, Prometheus, Grafana, and Kubernetes, the boilerplate aims to significantly reduce setup time and complexity. Key features like stage-based deployment, model versioning, and rolling updates enhance reliability and maintainability. The provided scripts for quick setup and deployment further simplify the process, making it accessible even for those with limited Kubernetes experience. The author's call for feedback highlights a commitment to addressing remaining pain points in ML deployment workflows.
Key Takeaways
Reference
“Infrastructure boilerplate for MODEL SERVING (not training). Handles everything between "trained model" and "production API."”