tensorflow-for-deep-learning-py/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.

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Learning References

Courses and Lists

• Data science resources.
• Lorte data pipelining.
• Incubator Airflow data pipelining
• Udemy's Airflow for Beginners.
• Awesome Airflow Resources.
• Airflow in Kubernetes.
• Astronomer: Airflow as a Service.
• Data pipeline samples.
• Awesome Scalability: a lot of articles and resources on the subject.
• Coursera's Big Data Pipeline course.
• Industrial Machine Learning Talk.

Enterprise Solutions

• Netflix data pipeline.
• Netlix data videos.
• Yelp data pipeline.
• Gusto data pipeline.
• 500px data pipeline
• Twitter data pipeline.
• Coursera data pipeline.
• Cloudfare data pipeline.
• Pandora data pipeline.
• Heroku data pipeline.
• Zillow data pipeline.
• Airbnb data pipeline.
• Walmart data pipeline.
• Robinwood data pipeline.
• Lyft data pipeline.
• Slack data pipeline.
• Remind data pipeline.
• Wish data pipeline.
• Databrick data pipeline.