Planning Strata - Githubissues

Azure / fast_retraining

Show how to perform fast retraining with LightGBM in different business cases

MIT License

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Planning Strata #1

Closed miguelgfierro closed 7 years ago

miguelgfierro commented 7 years ago

Planning kick off meeting. THAT IS THE FIRST MVP after this we iterate depending on the time we have

Methodology (based on TDSP):

Experiment in notebooks, common libraries in python
Develop in branches, merge to master via PR accepted by an external person (not by the commiter)
Try to do atomic commits in case anybody wants to cherry pick
In master there is always working code
FOCUS: get as fast as possible to MVP1 and then iterate.
When someone picks a task, you have to assign it to yourself and change the state in Projects

TODO MVP1:

[x] Start adding cards in the project github
[x] ask Ned about airline and sensor datasets
[x] set up a github private repo: Azure
[x] setup DSVM + libraries
[x] Put data in a fileshare
[x] timer class
[x] create conda environment variables for data and other stuff
[x] data loaders
[x] experiments with lightgbm (sklearn api)
[x] compare with xgboost
[x] parameter tuning framework
[x] plotter of model vs retrained model performance