What's included

• MLOps Architecture & Plan

  A clear system design covering data flow, training pipeline, model registry/versioning, deployment approach, and monitoring strategy.

• Reproducible Training Pipeline

  Modular ML/DL training pipeline (data prep → train → evaluate) with configurable parameters and repeatable runs.

• Experiment Tracking & Model Versioning

  Setup for tracking experiments, metrics, artifacts, and model versions (e.g., MLflow/DVC) to ensure reproducibility.

• Model Evaluation Report

  Evaluation summary with metrics, validation results, error analysis, and recommended improvements.

• Deployment-Ready API (Dockerized)

  A production-ready inference API (FastAPI/Flask) packaged with Docker for consistent deployment.

• CI/CD Automation

  Automated build/test/deploy pipeline (e.g., GitHub Actions) to ship updates reliably and reduce manual work.

• Monitoring & Logging Setup

  Monitoring-ready integration (logs + metrics + basic drift hooks/alerts) for ongoing reliability in production.

• Documentation & Handover

  Clear README + runbook (setup, training, deployment, troubleshooting) with a guided handover session.