Subscribe to the Machine Learning Engineer Newsletter

Receive curated articles, tutorials and blog posts from experienced Machine Learning professionals.

Issue #106
We wish happy holidays to all our MLE Newsletter subscribers!!
This week in Issue #106:
Forward  email, or share the online version on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
TryoLabs release their 6th annual Python libraries list. This year's list highlights general and data science related libraries that are growing in popularity in 2020, as well as currently well maintained and generally worth checking out.
A comprehensive article containing a curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and access to source code.
NumPy is a fundamental library for data processing. Understanding how NumPy works can give a boost to your skills. This article provides an intuitive overview of the concepts and features around the NumPy library leveraging a broad range of visual resources.
Insightful article from the Paypal Engineering team showcasing the journey at Paypal. They outline the evolution of their team  /infrastructure, their priorities across quarters, and the high level architecture behind their Enterprise Data Catalogue.
The LLVM team showcases how practitioners can leverage dynamic eval-style programming capabilities of the Cling C++ interpreter, which enable for interactive jupyter notebooks with C++ as the core language. This allows for practitioners to leverage the efficiency and speed of C++ whilst getting access to the dynamic experimentation properties that jupyter notebooks provide, and which are important in iterative/experiment-heavy contexts, such as in the data science examples showcased.
&nb sp;
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
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:
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
© 2018 The Institute for Ethical AI & Machine Learning