Uber's Michelangelo vs. Netflix's Metaflow

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  Uber's Michelangelo vs. Netflix's Metaflow Michelangelo Pain point Without michelangelo, each team at uber that uses ML (that’s all of them - every interaction with the ride or eats app involves ML) would need to build their own data pipelines, feature stores, training clusters, model storage, etc.  It would take each team copious amounts of time to maintain and improve their systems, and common patterns/best practices would be hard to learn.  In addition, the highest priority use cases (business critical, e.g. rider/driver matching) would themselves need to ensure they have enough compute/storage/engineering resources to operate (outages, scale peaks, etc.), which would results in organizational complexity and constant prioritization battles between managers/directors/etc. Solution Michelangelo provides a single platform that makes the most common and most business critical ML use cases simple and intuitive for builders to use, while still allowing self-serve extensibi...
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Rails on heroku

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Just found out that the new Rails 3.1.3 can be deployed to heroku using 'heroku create --stack cedar' instead of 'heroku create'.  By doing this, the only thing you have to change in the Gemfile is delete the line 'gem sqlite3' and add the following:

group :development, :test do
  gem 'sqlite3'
end

group :production do
  gem 'pg'
end

then commit and push to heroku.

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