Project Title: Packaging Machine Learning Models

Objective:

To prepare and package machine learning models for deployment, ensuring they are reusable, reproducible, and easy to integrate into applications or APIs.

Key Components:

Model Training:

A machine learning model (e.g., regression or classification) is trained using standard libraries like scikit-learn, TensorFlow, or PyTorch.

Data preprocessing (e.g., normalization, encoding) is handled via pipelines to ensure consistency during training and inference.

Serialization:

The trained model is saved using serialization libraries like:

joblib or pickle for scikit-learn

SavedModel format for TensorFlow

torch.save for PyTorch

Preprocessing pipelines are serialized alongside the model.

Environment Management:

Dependencies are tracked using requirements.txt, conda.yml, or Dockerfiles.

Versioning ensures reproducibility across environments.

Packaging:

Models are wrapped into a Python package or structured project directory.

Optionally includes CLI (command-line interface) for interaction.

Deployment Preparation:

Flask or FastAPI is used to create RESTful APIs for serving predictions.

Docker containers are built for platform-independent deployment.

Unit tests ensure functionality remains intact after packaging.

Documentation:

Clear instructions are provided for users on installing, running, and making predictions with the model package.

Outcome:

A well-documented, tested, and portable machine learning solution that can be deployed in production or shared with other teams.