The MachineLearning EngineerIssue #110

This week in Issue #110:

• Entire CompSci Curriculum in Youtube
• From MLOps to MLOops
• 2020’s Top ML Papers
• Python Data Science Startups
• The “Simplest” NumPy Course
• 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!

Entire CS Curriculum in Youtube

Great initiative which has attempted to create an entirer Compute Science curriculum using only Youtube videos. The scope of this course aims to cover every skill essential for a Software Engineer, and provides further specialisations in subfields like ML, Discrete Maths, etc.

From MLOps to MLOops

Awful AI is a curated list to track current scary usages of AI. The authors put together this open source list, hoping to raise awareness to its misuses in society, as well as encourage best practices.

2020’s Top ML Papers

Metamaven CTO Mariya Yao has shared their list of top 2020’s AI research papers. This article includes a very comprehensible brief of each of the 10 papers provided.

Python Data Science Startups

The TalkPython podcast brings together a group of Python data science startup founders to share insights aound their journey. This podcast includes Ines Montani from Explosion AI, Matthew Rocklin from Coiled, Jonathon Morgan from Yonder AI and William Stein from Cocalc

The “Simplest” NumPy Course

Data Scientist Ben Gorman has released a very comprehensive free course that introduces NumPy from scratch. This course’s core principle is that it was designed to be “so easy one’s grandma could learn it”.

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!