What is data privacy, really, and what tools are required for it? by Ernest Martinez via @infomgmt

Data privacy requirements necessitate not only identifying the location and nature of impacted data, but also the flow and transformation that it takes throughout the application landscape.

A great explanation and worth a read just to make sure you really understand the topic.

How to build pipelines with pandas using pdpipe by Tirthajyoti Sarkar via @TDataScience

This tutorial describes how to build intuitive and useful pipelines with pandas DataFrames using the pdpipe library.

A great tutorial which includes some code too. Definitely worth a bookmark.

An introduction to Kubernetes by/via @jeremyjordan

This is a great blog which will tell you what it is. How to use it. What it’s good for.

This is a perfect place to start learning about Kubernetes and thinking about what you can use it for. There are great code extracts as well as a list of useful links at the bottom.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead by Adrian Colyer via @kdnuggets

The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.

I enjoyed this article and his points which are very relevant.

The Problem with “Biased Data” by Harini Suresh via @Medium

Poorly defined terminology could actually play a role in biased data, says Harini Suresh. “The right terminology forms a mental framework, making it that much easier to identify problems, communicate, and make progress. The absence of such a framework, on the other hand, can be actively harmful, encouraging one-size-fits-all fixes for ‘bias,’ or making it difficult to see the commonalities and ways forward in existing work.”

I like this great article by Harini Suresh. I have noticed that you need to have an agreed set of definitions for all the data fields, the calculations, the methodologies, and even the data sources because that there are so many synonyms and opposing definitions for all of those that you need to measure like with like in the same way if you want to try and avail bias - if you do not you have already lost the battle.

WEBINAR: From Degas to Dashboards: Lessons of the Great Masters - 17 December 2019

Data Science Central Webinar Series Event

From Degas to Dashboards: Lessons of the Great Masters Join us for this latest DSC Webinar on December 17th, 2019 For over 30,000 years, we have expressed ourselves through visual art, and there are lessons we can draw from painting and apply them to viz. What do Impressionists teach us about dashboard interactivity? How does Cubism help us tell a data story? Set against a canvas of art history, in this latest Data Science Central webinar we will learn a dozen specific techniques and tools for building meaningful, engaging, and visually striking dashboards. Speaker: Jeff Pettiross, User Experience Designer -- Tableau Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  From Degas to Dashboards: Lessons of the Great Masters Date:  Tuesday, December 17th, 2019 Time:  9:00 AM - 10:00 AM PST   Space is limited so please register early: Reserve your Webinar seat now

Deep learning has hit a wall by Alex Woodie via @datanami

“The rapid growth in the size of neural networks is outpacing the ability of the hardware to keep up,” said Naveen Rao, vice president and general manager of Intel’s AI Products Group. Solving the problem will require rethinking how processing, network, and memory work together.

This sounds like a physical limitation that needs a two-pronged approach - one needs to be hardware advances but the other is an adaptation to the tools and techniques used to do AI and deep learning.