TMLS2020: Difference between revisions

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=== Bonus Workshop: How to Automate Machine Learning With GitHub Actions ===
=== Bonus Workshop: How to Automate Machine Learning With GitHub Actions ===
== 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/

Revision as of 16:34, 17 November 2020

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

Managing Data Science in the Enterprise

who:


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

Link dump