There are several reasons. In no particular order:
- I like the message in the tweet. I resonate with it.
- I like the link – typically a pointer to good reading material
- Because it provides a different point of view
- I use it as a marker in my life – a part of my daily log
- It is a hat tip to the author who causes me to pause and think.
- I think this tweet requires recognition and I would like to spread the idea
- It may be a part of a discussion. I jump in and do my two bits.
- It may be an event and I want to share it (a picture, a quote, a sound bite)
- Same reason I tweet – to start a conversation
- Same reason I tweet – to ask a question
This is one of the most comprehensive engineering blog posts on how Uber uses Machine Learning (ML) at scale. It covers:
- Uber’s ML Platform – Michael Angelo
- Uber’s research and production efforts and how they inter-relate
- How Uber achieves Model Developers Velocity
I made a list of few terms and concepts from the article:
- ML deployment use cases
- Pervasive deployment of ML in several applications
- Distributed training of ML
- Aligning ML applications with Uber’s priorities
- ML tools across the company (where and what)
- Internal events like – ML conferences, ML reading groups, talk series
- Data Science Workbench (a tool to build and iterate ML models)
- ML Platform team and how they work to support ML development inside Uber
- Technology stacks – Spark, Cassandra, Python and others
- Experiments with external tools both open source and commercial
- Uber’s open source contributions
It is nice to know how a dynamic company uses Machine Learning. There is a lot to learn from here. If you are thinking about building and deploying ML applications Scaling Machine Learning at Uber with Michelangelo | Uber Engineering Blog is a must read. I may go back and read it again.