corusm / mlops-project

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MLOps Project Group 62

Project Goal

The main goal of this project is to forecast wind power production at the Klim Windfarm.

Used frameworks

The project will initially employ the pytorch-forecasting library for model construction. Due to limited experience with this framework, there is consideration for a potential transition to PyTorch Lightning, known for its simpler model implementation approach compared to standard PyTorch.

Data

We will use the following dataset -> http://www.imm.dtu.dk/courses/02427/comp_ex_4_scripts_2011.zip. However, we intend to migrate to an alternative dataset that provides a continuous stream of new and diverse data.

Models

We expect to use some kind of auto-regressive model like RNN, LSTM or Transformer. Variations might be interesting too for better forecasting results, like the Temporal Fusion Transformer.

Project structure

The directory structure of the project looks like this:

├── Makefile             <- Makefile with convenience commands like `make data` or `make train`
├── README.md            <- The top-level README for developers using this project.
├── data
│   ├── processed        <- The final, canonical data sets for modeling.
│   └── raw              <- The original, immutable data dump.
│
├── docs                 <- Documentation folder
│   │
│   ├── index.md         <- Homepage for your documentation
│   │
│   ├── mkdocs.yml       <- Configuration file for mkdocs
│   │
│   └── source/          <- Source directory for documentation files
│
├── models               <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks            <- Jupyter notebooks.
│
├── pyproject.toml       <- Project configuration file
│
├── 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
|
├── requirements_dev.txt <- The requirements file for reproducing the analysis environment
│
├── tests                <- Test files
│
├── mlops_project  <- Source code for use in this project.
│   │
│   ├── __init__.py      <- Makes folder a Python module
│   │
│   ├── data             <- Scripts to download or generate data
│   │   ├── __init__.py
│   │   └── make_dataset.py
│   │
│   ├── models           <- model implementations, training script and prediction script
│   │   ├── __init__.py
│   │   ├── model.py
│   │
│   ├── visualization    <- Scripts to create exploratory and results oriented visualizations
│   │   ├── __init__.py
│   │   └── visualize.py
│   ├── train_model.py   <- script for training the model
│   └── predict_model.py <- script for predicting from a model
│
└── LICENSE              <- Open-source license if one is chosen

Diagram

Created using mlops_template, a cookiecutter template for getting started with Machine Learning Operations (MLOps).

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