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Update README.md (file structure)
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castolpe authored Nov 8, 2024
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Expand Up @@ -6,7 +6,7 @@ This project was created as part of the MLOps boot camp. The project was used to
**Developer Team**
- Asma Heena Khalil
- Ringo Schwabe
- Carolin Stolpe
- Carolin Stolpe (@castolpe]

Business Objectives
------------------
Expand All @@ -19,41 +19,58 @@ Architecture

File structure
------------

β”œβ”€β”€ LICENSE
β”œβ”€β”€ README.md <- The top-level README for developers using this project.
β”œβ”€β”€ data
β”‚Β Β  β”œβ”€β”€ interim <- Intermediate data that has been transformed.
β”‚Β Β  β”œβ”€β”€ processed <- The final, canonical data sets for modeling.
β”‚Β Β  └── raw <- The original, immutable data dump.
β”‚
β”œβ”€β”€ logs <- Logs from training and predicting
β”‚
β”œβ”€β”€ models <- Trained and serialized models, model predictions, or model summaries, tracked by DVC
β”‚
β”œβ”€β”€ requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
β”‚ generated with `pip freeze > requirements.txt`
β”‚
β”œβ”€β”€ src <- Source code for use in this project.
β”‚Β Β  β”œβ”€β”€ __init__.py <- Makes src a Python module
β”‚ β”‚
β”‚Β Β  β”œβ”€β”€ data <- Scripts to download or generate data
β”‚Β Β  β”‚Β Β  β”œβ”€β”€ check_structure.py
β”‚Β Β  β”‚Β Β  β”œβ”€β”€ import_raw_data.py
β”‚Β Β  β”‚Β Β  └── make_dataset.py
β”‚ β”‚
β”‚Β Β  β”œβ”€β”€ features <- Scripts to turn raw data into features for modeling
β”‚Β Β  β”‚Β Β  └── build_features.py
β”‚ β”‚
β”‚Β Β  β”œβ”€β”€ models <- Scripts to train models and then use trained models to make
β”‚ β”‚ β”‚ predictions
β”‚Β Β  β”‚Β Β  β”œβ”€β”€ predict_model.py
β”‚Β Β  β”‚Β Β  └── train_model.py
β”‚ β”‚
β”‚Β Β  β”œβ”€β”€ visualization <- Scripts to create exploratory and results oriented visualizations
β”‚Β Β  β”‚ └── visualize.py
β”‚Β Β  └── config <- Describe the parameters used in train_model.py and predict_model.py

β”œβ”€β”€ .dvc <- Configuration of the data version control
β”œβ”€β”€ .github
β”‚Β Β  └── workflows <- Github Actions to trigger CI/CD pipeline and data pipeline
|
β”œβ”€β”€ data
β”‚Β Β  └── interim <- Intermediate data that has been transformed.
β”‚Β Β  └── processed <- The final, canonical data sets for modeling.
β”‚Β Β  └── raw <- The original, immutable data dump.
β”‚Β Β  └── status.txt <- Indicator, if current data is valid.
β”‚
β”œβ”€β”€ logs <- Logs from training and predicting
β”‚
β”œβ”€β”€ metrics <- Metrics from the evaluated model.
|
β”œβ”€β”€ models <- Trained and serialized models, model predictions, or model summaries
|
β”œβ”€β”€ monitoring <- All files related to the monitoring of the application.
β”‚Β Β  └── alertmanager <- Configuration of the alert manager to inform in the event of deviations.
β”‚Β Β  └── grafana <- Configuration of the Grafana dashboard to visualize the metrics collected by Prometheus.
β”‚Β Β  └── prometheues <- Configuration to collect metrics about the health status of the app, number of requests etc.
β”‚
β”œβ”€β”€ notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
β”‚ the creator's initials, and a short `-` delimited description, e.g.
β”‚ `1.0-jqp-initial-data-exploration`.
β”‚
β”œβ”€β”€ references <- Data dictionaries, manuals, and all other explanatory materials.
β”‚
β”œβ”€β”€ reports <- Generated analysis as HTML, PDF, LaTeX, etc.
β”‚Β Β  └── figures <- Generated graphics and figures to be used in reporting
β”‚
β”œβ”€β”€ src <- Source code for use in this project.
β”‚Β Β  └── api <- Definiton of API endpoints
β”‚ β”‚
β”‚Β Β  └── data_module_def <- Scripts to download, validate and transform data
β”‚ β”‚
β”‚Β Β  └── models_module_def <- Scripts to train models and then use trained models to make
β”‚ β”‚ predictions
β”‚ └── pipeline_steps <- Scripts for the single pipeline_steps from data download to model evaluation
β”‚ β”‚
β”‚ └── utils <- Helper functions and other utils (e.g. logger)
β”‚Β Β  └── visualization <- Scripts to create exploratory and results oriented visualizations
β”‚Β Β  β”‚ └── visualize.py
| └── config_manager.py <- Create configuration objects for each of the stages
β”‚Β Β  └── config.py <- Paths to the config files
β”‚Β Β  └── config.yaml <- Values for the required configuration fields
β”‚Β Β  └── entity.py <- Definition of the config fields
| dvc.lock <- Locks of the last pipeline run
| dvc.yaml <- Orchestration of pipeline steps (DAG).
β”œβ”€β”€ LICENSE
β”œβ”€β”€ README.md <- The top-level README for developers using this project.
β”œβ”€β”€ requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
β”‚ generated with `pip freeze > requirements.txt`
--------

## How to run locally
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