The MachineLearning EngineerIssue #108

This week in Issue #108:

• Harvard Introduction to CompSci
• ML & DL Compendium 2017-2020
• NYU Deep Learning with PyTorch
• Python & Jupyter in Excel (Yep)
• Learning NLP the Practical Way
• 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!

Harvard Introduction to CompSci

Harvard’s computer science course is available for free for anyone looking to brush up or strengthen their software engineering foundations. This course includes algorithms, data structures, security and has several practical webapp development examples.

ML & DL Compendium 2017-2020

AXA Global Head of Tech Innovation Ori Cohen has put together a fantastic 390-page document containing chunk-sized referenceable information ranging across a broad range of topics in machine / deep learning.

NYU Deep Learning with PyTorch

Fantastic online course consisting of the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Python & Jupyter in Excel (Yep)

Software Developer Tony Roberts is bridging the gap between data science and traditional Excel analysis providing access to the flexibility of Jupyter notebooks (and Python). Innovations like this can only open the doors to greater innovations - I do look forward to the robust productionisation process for these Jupyter Notebooks (or Excel sheets) required to ensure scalable end-to-end advanced analytics capabilites, aka MLOps… or more specifically AIExcelOps?

Learning NLP the Practical Way

FinText Founder Vered Zimmerman has put together a comprehensive guide to dive into NLP in a practical way. This article covers the journey through the NLP ecosystem covering the foundational must-have-s, key datasets to play with, shallow reading resources, deep reading resources, libraries, projects and beyond.

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!