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

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.

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