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👾 my old deep learning notebooks (e.g., tensorflow examples, caffee, deep art, numpy)
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Mia Steinkirch b43eaf587c 🛳 Remove workflows, since there is not point
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.github 🛳 Remove workflows, since there is not point 2019-10-27 15:09:25 -07:00
machine_learning_examples 👾 Add some machine learning experiments 2019-10-27 15:05:11 -07:00
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airflow.md 💎 Add some references for Airflow 2019-10-27 15:04:26 -07:00
README.md 🌊 Clean up readme 2019-10-27 15:08:27 -07:00

README.md

Curated Resources on ETL, Machine Learning, and ML Pipelines

The morale of this repository is to cover resources for deploying Machine learning
in production environments, a task that includes 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