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README.md

Curated Resources on ETL, Machine Learning, and ML Pipelines

The morale of this repository is the fact that Machine learning involves tasks that include data sourcing, data ingestion, data transformation, pre-processing data for use in training, training a model and hosting 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.

Learning References

Courses and Lists

Enterprise Solutions