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A microservice stack for training and running models for forecasting of oil production

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BDI-UFRGS/MLFlow-TimeSeries-Oil-Stack

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MLFlow Oil Production Time-Series Forecast Stack

This repository contains a microservice stack based on MLFlow for training and executing Oil production time-series forecasting Machine-Learning Models. It is a proposal for a digital twin module.

  1. The project tree has a docker-compose.yml for deployment in a docker swarm. The architecture consists of:
    • mlflow_client
      • app
        • service

          • src
            • forecast.py: API service that receives user parameters and executes the MLProject backcast in projects/train_inference
        • main.py: main application file that executes and keeps all FastAPI endpoints

      • projects
        • train_inference
          • train.py: Trains forecasting models
          • backcast.py: Executes model inference
          • utils.py: Keeps utility functions used throughout the code
          • config.yml: Contains all adjustable parameters for model training (hyperparameter ranges, size of windows, num of trials, etc)
          • data_types.yml: Contains a dictionary with a datatype of each hyperparameter
          • registered_models.yml: Contains a list of registered models for each combination of lookback, horizon and sampling frequency
      • requirements
      • tests
    • mlflow_tracking
    • mysql
    • minio

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A microservice stack for training and running models for forecasting of oil production

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