Gradient Flow #31: AI in Healthcare, Data Quality, Understanding Neural Networks

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This edition has 368 words which will take you about 2 minutes to read.

“There’s a Fog of War, but there’s also a Fog of Peace.” – Eric Grosse

Data Exchange podcast

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We recently conducted a survey to understand how leading Healthcare, Biotech, and Pharmaceutical companies are building AI and Machine Learning products and services.  The survey drew close to 400 respondents from 49 countries. Grab your report on the survey results below:


Data & Machine Learning Tools and Infrastructure

  • Toward Confidential Cloud Computing   This ACM paper describes the state of tools for extending “hardware-enforced cryptographic protection” to data products and services. (I recommend you read the pdf version.)
  • Evidently  Open source library for analyzing machine learning models during development, validation, or production monitoring.
  • OpenMMLab  Open source, deep learning models for computer vision from the Chinese unicorn, SenseTime.
  • Apache Airflow 2020 User Survey    Airflow is one of the more popular tools among data engineers and is used to build, schedule, and monitor workflows. Celery is the most popular option to execute Airflow, with Kubernetes placing second.
  • Everyone with a data pipeline has data quality issues   Notes from interviews with data engineering teams at mid to large-size companies.
  • Data Quality at Airbnb  The authors cover architecture, tools, organizational structure, and best practices. A followup post details how they design and build data pipelines.

Funding Updates

[Photo by Arnaud Mariat on Unsplash]


Closing Short: Learn why Ikaria is one of only five Blue Zones in the world. Blue Zones have a high percentage of people who live past 90 years old.

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