The MachineLearning EngineerIssue #68

This week in Issue #68:

• GitLab Data Lessons Learned
• Data Discovery at Spotify
• Exploratory Data Analysis Deep Dive
• Tokenisers & How Machines Read
• Intel Demystifying the AI Stack
• 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!

GitLab Data Lessons Learned

A fantastic article by GitLab Staff Data Engineer Taylor Murphy on his key lessons learned leading the GitLab Data Team. In this article Taylor covers the importance of management skills in data engineering, together with key areas to focus including growth, hiring, process, tools, performance, meetings and beyond. The article also provides a significant amount of links and resources to expand in these very useful areas.

Data Discovery at Spotify

Spotify has released a high level overview of their journey to improve data discovery across the firm. In this article they provide a set of high level steps (or themes) that they follow to achieve these; including diagnosing the problem, understanding intent, enabling knowledge, mapping expertise and more.

Exploratory Data Analysis Dive

A very comprehensive article that outlines the best practices on Exploratory Data Analysis, a step which is foundational to the data science process. This article covers a high level definition to EDA, it’s components, and a deep dive into how to dive into understanding features, cleaning datasets and analysing feature relationships.

Tokenisers & How Machines Read

NLP applications are only growing in industry, and hence best practices and understanding of its fundamentals is increasingly crucial. This article provides a very comprehensive deep dive in one of the core components of NLP; tokenization. This article provides a deep dive on the topic of tokenization, together with the challenges that present in this space, and common types of text tokenization.

Intel Demystifying the AI Stack

Intel Capital has created an overview of the end-to-end AI infrastructure stack, together with a mapping of how existing projects fall into their respective categories. In this article they cover an overview of the different layers of their Stack, including Hardware, Software Accelerators, Libraries, Data Science Frameworks, Orchestration, Automation, and Autonomous.

OSS: Data Visualisation

The topic for this week’s featured production machine learning libraries is Data Visualisation. 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:

• Redash - Redash is anopen source visualisation framework that is built to allow easy access to big datasets leveraging multiple backends.
• Plotly Dash - Dash is a Python framework for building analytical web applications without the need to write javascript.
• Streamlit - Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file
• PDPBox - This repository is inspired by ICEbox. The goal is to visualize the impact of certain features towards model prediction for any supervised learning algorithm. (now support all scikit-learn algorithms)

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 thiese 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. We will be showcasingitg three resources from our list so we can check them out every week. This week’s resources are:

• 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 UK-based research centre that carries out world-class research into responsible machine learning systems.

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