MLOps

This repository will have all the details about the course, each week the instructors will be including the corresponding material, sou you will be able to follow along the details of each topic.

At the begining of the course, you only see the following project structure:

├── LICENSE            <- Open-source license if one is chosen
├── README.md          <- The top-level README for developers using this project.
├── docs               <- This is where weekly lecture slides, detailed session notes,
|                         and complementary resources for each topic will be stored.

However, as we move forward with the course, it is expected to have the follosing details.

Project Organization

├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- This is where weekly lecture slides, detailed session notes,
|                         and complementary resources for each topic will be stored.
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-rv-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         mlops and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── mlops   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes mlops a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    └── plots.py                <- Code to create visualizations

---

Note:

• The docs/ directory will not only include the default MkDocs structure, but it will also serve as the main location for weekly lecture slides, detailed notes, and complementary resources for each session of the course.

• The notebooks/ directory will contain all Jupyter notebooks used throughout the course. Each notebook will reflect the step-by-step progress of the topics when a notebook is needed.