Development of Data Science Ecosystem for Ruby
summary
Ruby calls Python via PyCall to run real tools: code benchmarks 100 iterations, collects results into a pandas DataFrame, and saves a seaborn bar chart PNG; a Rails demo converts ActiveRecord into DataFrames and embeds visualizations in HTML. Deep learning uses a TensorFlow-backed SSD300 model via the same bridge and writes annotated output images on CPU. Work is underway to add Apache Arrow bindings (red-data-tools) so native high-speed data exchange becomes possible.
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chapters (10)
- 0:00Introduction to data science in Ruby
- 3:47Current state of Ruby in data science
- 6:49Introducing PyCall for Python integration
- 11:38Demo benchmarking with PyCall
- 13:59Demo Rails app with Pandas
- 22:00Demo object detection with deep learning
- 28:33Future of Ruby in data science
- 31:08Red Data Tools project overview
- 35:02Call to action and workshop info
- 37:09Q&A session
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