TMLS2020

Notes from chat channels

What are people working on?

https://medium.com/alphabyte-research-lab/tracking-parliament-with-machine-learning-part-1-background-35655ac91bca

Nov 16th

Workshops

Topic: Workshop: MLOps & Automation Workshop: Bringing ML to Production in a Few Easy Steps

Time: Nov 16, 2020 09:00 AM Eastern Time (US and Canada)

who: Yaron Haviv https://medium.com/@yaronhaviv

Tools:

• mlrun - lots of end to end demos /demos
• nuclio
• kubeflow

What it gives us:

• CICD for ML
• auditability
• drift
• feature store for meta data, and drift.

Topic: Reaching Lightspeed Data Science: ETL, ML, and Graph with NVIDIA RAPIDS

Time: Nov 16, 2020 11:00 AM Eastern Time (US and Canada)

https://github.com/dask/dask-tutorial

who: Bradley Rees ( from Nvidia )

https://medium.com/rapids-ai/gpu-dashboards-in-jupyter-lab-757b17aae1d5

https://nvidia.github.io/spark-rapids/

file formats:

h5 - for gentic info.

h5ad - compressed annotated version

nica lung

https://github.com/rapidsai-community/notebooks-contrib/blob/branch-0.14/conference_notebooks/KDD_2020/notebooks/Lungs/hlca_lung_gpu_analysis.ipynb

genetic analysis, 2d visualization

UMAP

• https://www.nature.com/articles/s41467-020-15351-4
• https://umap-learn.readthedocs.io/en/latest/basic_usage.html

Managing Data Science in the Enterprise

who:

• Randi J Ludwig , Sr. Manager Applied Data scientist - Dell Technologies
• Joshua Podulska - Chief Data scientist - Domino Data lab.

The age old “discipline” problem. This is not a data science problem and this is not a technology problem, this is a human problem.

"Paved paths"

kathy oneil weapons of math descruction

Nov 17th

Bonus Workshop: How to Automate Machine Learning With GitHub Actions

11:00 am

boring: what is docker? Skipped it.

Black-Box and Glass-Box Explanation in Machine Learning

who: Dave Scharbach who: rich cauruna

gradient boost decision tree

SHAP vales Shapley - the difference between the averge effect and the effect for the value.

linear model.

use "partial dependent plot " to see the effect of one input.

tree explainer for say GBM model.

two matrix: out matrix, and explainer matrix

output matrix has the same metrics, and explainer matrix has multple metrics.

bswarm plot

when you look at global summary stats, you wash away Rare high magnitude effect.

vertical dispersion.

interaction effect, agent and gender, there ar esome sublte bits,

we often see "treatment effects" if you BUN is higher than X then you get treatment Y, ergo the graph of BUN level has "notches" in it where treatments are triggered, drug .. dialysis, etc.

EBM - explainable booster machine - some in R and spark on the way.

Project: microsofts "interpret" package. EBM

https://github.com/interpretml/interpret

"How do you explain a model?"

also see SHAP package. https://github.com/slundberg/shap

shap.plots.waterfall() neato!

Link dump

• https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai

• https://blog.dominodatalab.com/measuring-data-science-business-value/

• https://www2.slideshare.net/MiguelFierro1/knowledge-graph-recommendation-systems-for-covid19