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# ETL, ML, and ML Pipelines # Machine Learning Pipelines
``` ```
In this repository we cover resources for deploying Machine learning In this repository we cover resources for deploying Machine learning
@ -7,18 +7,17 @@ transformation, pre-processing data for use in training, training a model, and h
the model. the model.
``` ```
Three conceptual steps are how most data pipelines are designed and structured:
* **Extract**: sensors wait for upstream data sources.
* **Transform**: business logic is applied (e.g. filtering, grouping, and aggregation to translate raw data into analysis-ready datasets).
* **Load**: processed data is transported to a final destination.
# Resources in this repository # Resources in this repository
### Machine Learning Science
* [Deep Learning](https://github.com/bt3gl/Curated_ETL-and-ML-Pipelines/blob/master/deep_learning_resources.md). * [Deep Learning](https://github.com/bt3gl/Curated_ETL-and-ML-Pipelines/blob/master/deep_learning_resources.md).
### Machine Learning Infrastructure
* [Airflow](https://github.com/bt3gl/Curated_ETL-and-ML-Pipelines/blob/master/airflow.md). * [Airflow](https://github.com/bt3gl/Curated_ETL-and-ML-Pipelines/blob/master/airflow.md).
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# External Resources # External Resources