chandan-aiops 1.0.0

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Chandan AIOps:

A Python package to instantly generate a standardized, production-ready MLOps/AIOps project structure with one command. This tool scaffolds a complete AI/ML project following best practices, saving you hours of setup time.

Features:

• One-Command Setup: Create a full-featured AI/ML project instantly.

• Production-Ready Template: Includes configuration, logging, Docker, CI/CD, and more.

• Standardized Structure: Enforces a clean, scalable, and maintainable folder layout.

• Best Practices Included: Pre-configured for MLflow (experiment tracking), DVC (data versioning), FastAPI, and testing.

• Extensible: Use the generated structure as a solid foundation for any AI project.

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PyPI Project Page: https://pypi.org/project/chandan-aiops/

pip package : pip install chandan-aiops

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1. pip install chandan-aiops

2. aiops-create my_ai_project

3. Designed Code Structure: 

my_ai_project/
├── data/                   # Data management
│   └── raw/               # Store raw, immutable data here
├── data_insights/         # For exploratory data analysis (EDA) reports
├── models/                # Serialized trained and deployed models
├── mlruns/                # MLflow experiment tracking (auto-created)
├── logs/                  # Application and pipeline logs
├── research/              # Jupyter notebooks for experimentation
├── src/                   # Main source code (pipelines, training, etc.)
│   ├── __init__.py
│   ├── data_ingestion.py
│   ├── data_preprocessing.py
│   ├── model_builder.py
│   ├── model_evaluator.py
│   ├── model_predictor.py
│   └── logger.py          # Centralized logging setup
├── app/                   # FastAPI web application
│   ├── __init__.py
│   ├── main.py           # FastAPI app instance
│   ├── schemas.py        # Pydantic models
│   ├── service.py        # Business logic
│   ├── templates/
│   │   └── index.html    # Example frontend template
│   └── static/
│       └── style.css     # Example static file
├── tests/                 # Unit and integration tests
│   └── test_pipeline.py
├── config.py             # Central configuration (paths, settings)
├── main.py               # Main script to run the ML pipeline
├── Dockerfile           # For containerizing the application
├── dvc.yaml             # Data Version Control pipeline definition
├── .dvcignore           # Files for DVC to ignore
├── .github/             # GitHub Actions CI/CD workflows
│   └── workflows/
│       └── ci.yml
├── pyproject.toml       # Project dependencies and metadata
└── README.md            # Your project's own README

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