Trained and validated ML models (or fine-tuned foundation models) with versioned artifacts
End-to-end ML pipelines for data preprocessing, training, evaluation, and inference
Integrated deployment (API, service, or edge integration) ready for production use
Performance and evaluation reports (accuracy, latency, robustness, edge cases)
Monitoring and logging setup for model performance and drift
CI/CD workflows for model and service updates
Clear technical documentation (architecture, APIs, training process, limitations)
Handoff and knowledge transfer for maintenance and future iteration