The MachineLearning EngineerIssue #107

This week in Issue #107:

• 2020 Year Review Papers with Code
• Machine Learning going Realtime
• An eXplainability toolbox for ML
• Simplifying Outlier Detection
• Public Engineering Career Ladders
• Featured OSS Production ML Libraries
• Awesome AI Guidelines to check out this week
• + more 🚀

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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone’s support!

Year Review Papers with Code

Papers with Code indexes various machine learning artifacts (papers, code and results) to facilitate discovery and comparison. Using this data they can get a sense of what the ML community found useful and interesting this year. In this article they summarize the top trending papers, libraries and benchmarks for 2020 on Papers with Code.

Machine Learning going Realtime

This article covers the topic of real time machine learning in a comprehensive overview. It breaks it down into two levels of real-time machine learning, namely: 1) ML system makes predictions in real-time (online predictions), and 2) system can incorporate new data and update your model in real-time (online learning).

An eXplainability toolbox for ML

AI explainability techniques continue to become adopted throughout the multiple stages of the machine learning lifecycle. This Kaggle notebook provides a set of techniques that can be used to uncover potential undesired biases, and delves into concepts surounding demographic parity, disparate impact, equal opportunity, and equalized odds.

Simplifying Outlier Detection

Outlier detection is the identification of data set elements that vary significantly from the majority. Those elements are known as outliers, and there are various incentives for detecting them, depending on the context and domain of each case. This article provides a practical example on how outlier detection can be used in image classification use-cases.

Public Engineering Career Ladder

Career progression in the engineering world can be quite complex and ambiguous, however there are growing number of resources available online to provide guidance. This great resource has compiled the public engineering career ladders published by tech companies.

OSS: Metadata Management

The topic for this week’s featured production machine learning libraries is Metadata Management. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are:

• Amundsen - Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

• DataHub - DataHub is LinkedIn’s generalized metadata search & discovery tool.

• Metacat - Metacat is a unified metadata exploration API service. Metacat focusses on solving these three problems: 1) Federate views of metadata systems. 2) Allow arbitrary metadata storage about data sets. 3) Metadata discovery.

• ML Metadata - a library for recording and retrieving metadata associated with ML developer and data scientist workflows. Also TensorFlow ML Metadata.

If you know of any libraries that are not in the “Awesome MLOps” list, please do give us a heads up or feel free to add a pull request!

OSS: Awesome AI Guidelines

As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:

• AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems
• An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles
• ACM’s Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018
• From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices

If you know of any guidelines that are not in the “Awesome AI Guidelines” list, please do give us a heads up or feel free to add a pull request!

About us

The Institute for Ethical AI & Machine Learning is a Europe-based research centre that carries out world-class research into responsible machine learning.