| Name | batch-prediction-pipeline-self JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | |
| Summary | |
| upload_time | 2023-09-20 16:53:53 |
| maintainer | |
| docs_url | None |
| author | Nikhil |
| requires_python | >=3.9,<3.10 |
| license | |
| keywords |
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| requirements |
No requirements were recorded.
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# Batch Prediction Pipeline
Check out [Lesson 3](https://towardsdatascience.com/unlock-the-secret-to-efficient-batch-prediction-pipelines-using-python-a-feature-store-and-gcs-17a1462ca489) on Medium to better understand how we built the batch prediction pipeline.
Also, check out [Lesson 5](https://towardsdatascience.com/ensuring-trustworthy-ml-systems-with-data-validation-and-real-time-monitoring-89ab079f4360) to learn how we implemented the monitoring layer to compute the model's real-time performance.
## Install for Development
The batch prediction pipeline uses the training pipeline module as a dependency. Thus, as a first step, we must ensure that the training pipeline module is published to our private PyPi server.
**NOTE:** Make sure that your private PyPi server is running. Check the [Usage section](https://github.com/iusztinpaul/energy-forecasting#the-pipeline) if it isn't.
Build & publish the `training-pipeline` to your private PyPi server:
```shell
cd training-pipeline
poetry build
poetry publish -r my-pypi
cd ..
```
Install the virtual environment for `batch-prediction-pipeline`:
```shell
cd batch-prediction-pipeline
poetry shell
poetry install
```
Check the [Set Up Additional Tools](https://github.com/iusztinpaul/energy-forecasting#-set-up-additional-tools-) and [Usage](https://github.com/iusztinpaul/energy-forecasting#usage) sections to see **how to set up** the **additional tools** and **credentials** you need to run this project.
## Usage for Development
To start batch prediction script, run:
```shell
python -m batch_prediction_pipeline.batch
```
To compute the monitoring metrics based, run the following:
```shell
python -m batch_prediction_pipeline.monitoring
```
**NOTE:** Be careful to complete the `.env` file and set the `ML_PIPELINE_ROOT_DIR` variable as explained in the [Set Up the ML_PIPELINE_ROOT_DIR Variable](https://github.com/iusztinpaul/energy-forecasting#set-up-the-ml_pipeline_root_dir-variable) section of the main README.
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"description": "# Batch Prediction Pipeline\n\nCheck out [Lesson 3](https://towardsdatascience.com/unlock-the-secret-to-efficient-batch-prediction-pipelines-using-python-a-feature-store-and-gcs-17a1462ca489) on Medium to better understand how we built the batch prediction pipeline. \n\nAlso, check out [Lesson 5](https://towardsdatascience.com/ensuring-trustworthy-ml-systems-with-data-validation-and-real-time-monitoring-89ab079f4360) to learn how we implemented the monitoring layer to compute the model's real-time performance.\n\n## Install for Development\n\nThe batch prediction pipeline uses the training pipeline module as a dependency. Thus, as a first step, we must ensure that the training pipeline module is published to our private PyPi server.\n\n**NOTE:** Make sure that your private PyPi server is running. Check the [Usage section](https://github.com/iusztinpaul/energy-forecasting#the-pipeline) if it isn't.\n\nBuild & publish the `training-pipeline` to your private PyPi server:\n```shell\ncd training-pipeline\npoetry build\npoetry publish -r my-pypi\ncd ..\n```\n\nInstall the virtual environment for `batch-prediction-pipeline`:\n```shell\ncd batch-prediction-pipeline\npoetry shell\npoetry install\n```\n\nCheck the [Set Up Additional Tools](https://github.com/iusztinpaul/energy-forecasting#-set-up-additional-tools-) and [Usage](https://github.com/iusztinpaul/energy-forecasting#usage) sections to see **how to set up** the **additional tools** and **credentials** you need to run this project.\n\n## Usage for Development\n\nTo start batch prediction script, run:\n```shell\npython -m batch_prediction_pipeline.batch\n```\n\nTo compute the monitoring metrics based, run the following:\n```shell\npython -m batch_prediction_pipeline.monitoring\n```\n\n**NOTE:** Be careful to complete the `.env` file and set the `ML_PIPELINE_ROOT_DIR` variable as explained in the [Set Up the ML_PIPELINE_ROOT_DIR Variable](https://github.com/iusztinpaul/energy-forecasting#set-up-the-ml_pipeline_root_dir-variable) section of the main README.\n",
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