MLOps workflow with a Model Control Plane (MCP) server for model management and deployment
Overview
what is MLOps Model Control Plane Server?
MLOps Model Control Plane Server is a comprehensive solution for managing and deploying machine learning models through an MLOps workflow.
how to use MLOps Model Control Plane Server?
To use the server, clone the repository, install the dependencies, configure the settings, and run the application using provided commands for data processing, model training, and inference.
key features of MLOps Model Control Plane Server?
- Data processing and model training pipelines
- Model registry for versioning and lifecycle management
- FastAPI-based Model Control Plane (MCP) server
- Monitoring with Prometheus and Grafana
- Dockerized deployment for easy setup
use cases of MLOps Model Control Plane Server?
- Managing machine learning model versions and lifecycle
- Deploying models for inference in production environments
- Monitoring model performance and health
FAQ from MLOps Model Control Plane Server?
- What are the prerequisites for running the server?
You need Python 3.8+, Docker, and Docker Compose.
- How do I run the MCP server?
Use the command
python main.py --action serveto start the server.
- Can I monitor the models?
Yes, you can access Prometheus metrics and Grafana dashboards for monitoring.