ETL, ML, and ML Pipelines

In this repository we 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.

Subresources

• Deep Learning.
• Airflow.

External Resources

Tools & Code Samples

• Data science resources.
• AWS Data pipeline samples.

Lorte

• Lorte data pipelining.

MOOCs

General Pipelines

• Coursera's Big Data Pipeline course.

Tutorials & Articles

2019

• How to Code Neat Machine Learning Pipelines.

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.

Talks

• Industrial Machine Learning Talk.