I want a production-ready machine-learning model management system that wraps seamlessly around our current, cloud-based training workflows. All models we run—built in PyTorch and Scikit-Learn—must move through a single, automated MLflow pipeline that version-controls every run, registers each model, stores all artifacts in Google Cloud Storage, and pushes the chosen versions straight into an inference endpoint without manual hand-offs. Key Objectives & Required Features: The successful implementation of this project will provide the following capabilities, broken down into two main areas: 1. Model Lifecycle & Versioning: Model Lifecycle Management: A clear and structured process to track and manage models as they move from training to staging and finally to production. Robust Version Control: The system must be able to store and manage multiple versions of our machine learning models, allowing for easy access to previous iterations. Seamless GCS Integration: All model artifacts should be automatically stored in and loaded from a designated GCS bucket, acting as our central model repository. MLflow Registry Core: The solution must be built around MLflow's native model registry features to leverage its powerful tracking and management capabilities. Backward Compatibility: The new system must not break our existing model training workflows. The integration should feel like a natural extension of our current process. 2. Inference & Deployment: Automatic Model Registration: Once a model is trained, it should be automatically registered with the MLflow Model Registry. Dynamic Model Discovery: The inference service must be able to automatically discover and load the latest model version that has been promoted to the production stage. A/B Testing Capability: The system should allow for the deployment of different model versions simultaneously to different inference endpoints to facilitate A/B testing and performance comparison. One-Click Rollbacks: In case of performance degradation, we need the ability to quickly and easily roll back to a previously stable model version. Production Model Monitoring: The ability to easily track which specific model version is currently serving predictions in the production environment. Scope of Work & Deliverables: System Design: A brief design document outlining the architecture of the proposed MLOps pipeline. Infrastructure Setup: Configuration of MLflow Tracking Server and backend to use GCS for artifact storage. Training Script Integration: Modify existing training scripts to log models to MLflow and register them in the Model Registry. Inference Service API: Develop a Python-based REST API (using a framework like FastAPI or Flask) that can: Load and serve the latest production model by default. Load and serve a specific model version when provided. Be containerized (Dockerfile) for easy deployment. Automation Scripts: A simple CI/CD script or workflow (e.g., a shell script or a basic GitHub Actions workflow) to automate the train -> register -> deploy process. Documentation: Clear and concise documentation explaining the workflow, how to promote models, and how to deploy new versions or perform rollbacks. Required Skills: Proven experience in MLOps and building machine learning pipelines. Expert-level knowledge of MLflow, specifically the Model Registry and Tracking components. Strong proficiency with Google Cloud Platform (GCP), especially GCS. Experience in developing and containerizing Python-based REST APIs (FastAPI, Flask, Docker). Familiarity with CI/CD principles and tools.