In fall 2020, Twitter users raised concerns that the automated image cropping system on Twitter favored light-skinned over dark-skinned individuals, as well as concerns that the system favored cropping woman's bodies instead of their heads
They find systematic disparities in cropping and identify contributing factors, including the fact that the cropping based on the single most salient point can amplify the disparities
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Did you think bringing your machine learning model to production was the hard part?
What about model drift?
Now MLOps comes into play but how does it work and what are good tools?
What is:
- Continuous integration (CI)
- Continuous deployment (CD)
- Continuous training (CT)
The full MLOps life cycle
- Data Engineering: Get and clean the data recurring if necessary
- Model Engineering: Model training, evaluation, testing, and packaging
- Model Deployment: integrating the trained model. Model serving, performance monitoring
Why is MLOps important?
Just because your model is hitting now doesn't mean it will be doing so 6 months from now
Where the big ones like OneNote, Google Keep and Evernote fail is that the brain does not work like an index, thoughts are linked and associatively this is where the next generation of note taking apps show their strength.
Your open source project is ready for deployment? Documentation is still missing?
Good documentation and its presentation is an art!
A case study with 4 examples on awesome documentation
What makes good documentation?
- No prosaic texts! Choose a practical approach with code snippets
- Good structure and overview with a quick entry then in depth
- Good search is everything
- Good code examples
Why?
-Extremely good search
-These diagrams eye candy everywhere!
-Interactivity
-Live code examples that can be customized and run in a Binder container