Model Versioning

Set up a dedicated Git repository for model code and a separate artifact store (e.g., DVC, MLflow, or S3) to track model binaries, weights, and metadata. Configure .gitignore to exclude large model files from Git, and initialize DVC or MLflow tracking in the project root. This ensures that every experiment and model version is uniquely identified and retrievable.

Why MLEM: MLEM directly supports model packaging, saving, versioning, and registry, and integrates with cloud storage (S3/GCS) and Git/DVC workflows for artifact repository initialization.

Before training, create a run in MLflow or DVC that captures the exact code commit, dataset version, hyperparameters, and environment (e.g., Docker image or conda environment). Log all parameters programmatically using the tracking API. This step ensures that each model version is fully auditable and can be reproduced later.

Why MLflow: MLflow excels at experiment tracking and model versioning, allowing registration of training runs with metadata, and integrates with dataset versioning tools like DVC.

Execute the training script, and after training completes, save the model weights (e.g., .pt, .h5, .pkl) and any associated files (tokenizer, scaler, config). Use the tracking tool to log these artifacts along with metrics (accuracy, loss, F1) and plots (confusion matrix, learning curves). This creates a permanent record of the trained model and its performance.

Why MLflow: MLflow handles experiment tracking, model versioning, and artifact logging, making it ideal for training and logging model artifacts with a training framework.

After training, assign a semantic version tag (e.g., v1.0.0) to the Git commit and the artifact in the tracking system. Optionally, promote the model to a 'staging' or 'production' stage using MLflow's model registry or DVC's tag feature. This step formalizes the version and makes it easy to reference for deployment.

Why MLflow: MLflow Model Registry provides tagging, version promotion, and stage transitions (Staging/Production), integrating with Git and CI/CD pipelines.

Pull the tagged model artifact from the registry and deploy it to the target environment (e.g., a REST API server, edge device, or batch inference pipeline). Use a containerized deployment (Docker) with the exact environment captured earlier. Verify that the deployed model loads correctly and produces expected predictions on sample data.

Why MLEM: MLEM supports multi-platform deployment, packaging models for Docker/Kubernetes/cloud services, and integrates with MLflow clients.

Set up monitoring for the deployed model (e.g., prediction drift, latency, error rate) using tools like Prometheus or custom logging. If performance degrades, rollback to a previous version by redeploying the earlier tagged artifact. This step ensures production reliability and enables safe iteration.

Why MLflow: MLflow provides model versioning and evaluation capabilities, which can be used alongside monitoring tools for rollback decisions and alerting.