The MachineLearning EngineerIssue #98

This week in Issue #98:

• Andrew Ng on Production AI
• Netflix’s Distributed Tracing Infra
• Importance of Data in MLOps
• Image Outlier Detection in ML
• The State of AI Ethics Report
• 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!

Andrew Ng on Production AI

Andrew Ng discusses key challenges facing AI deployments and possible solutions, ranging from techniques for working with small data to improving algorithms’ robustness and generalizability to systematically planning out the full cycle of machine learning projects.

Netflix’s Distributed Tracing Infra

Production machine learning models introduce complex infrastructure complexities, which require more advanced approaches for monitoring and observability. Netflix provides insights into the knowledge obtained throughout building their distributed tracing infrastructure. Distributed tracing is the ability to measure performance and other metrics across microservice hops of requests as they travel through the system.

Importance of Data in MLOps

Machine Learning Operations continues to growing in importance. This post discusses the importance of data quality in MLOps workflows, as well as the flow of data involved across the diffeent components of the stack.

Image Outlier Detection in ML

Outlier detection is the identification of data set elements that vary significantly from the majority. When it comes to production systems this is a key tool to ensure sound performance of production models. This post introduces a practical use-case for image outlier detection.

The State of AI Ethics Report

Montreal AI Ethics Institute released a report which captures the most relevant developments in Q3 2020 in the domain of AI ethics across academia, civil society, government, and industry. It covers key areas such as AI & society, bias, disinformation, labour impacts, privacy, risk, and the future of AI ethics.

OSS: Industry Strength NLP

The topic for this week’s featured production machine learning libraries is Industry Strength NLP. 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:

• SpaCy - Industrial-strength natural language processing library built with python and cython by the explosion.ai team.
• Snorkel - Snorkel is a system for quickly generating training data with weak supervision https://snorkel.org.
• Transformers - Huggingface’s library of state-of-the-art pretrained models for Natural Language Processing (NLP).
• Github’s Semantic - Github’s text library for parsing, analyzing, and comparing source code across many languages.

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.