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Issue #97
This week in Issue #97:
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GPUs have proven extremely useful for highly parallelizable data processing use-cases. The computational paradigms found in machine learning & deep learning for example fit extremely well to the processing architecture graphics cards provide. This tutorial shows how it's possible to achieve 2x+ performance improvements when submitting independent GPU-intensive workloads by leveraging multi-queue operation parallelism using Vulkan and Kompute.
Jeremy Howard, FastAI Author delves into how bringing software engineering best practices, such as layered API design and decoupling, have allowed him to provide a deep learning library that is both easier to use for beginners, at the same time as being more deeply hackable for experts, and also increasing performance. He will be drawing from research discussed in the peer reviewed paper describing the principles of software engineering applied to deep learning.
Data-driven software is the future. This shift is bringing about a new category of tools—what we call model performance monitoring (MPM). This article provides an introduction to the topic of MLOps as well as the core pillars that contribute to its potential to become "the next billion dollar industry".
A fantastic article containing 200 resources for machine learning and Python, ranging across introductions, mathematical foundations, best practices, and a broad range of machine learning algorithms and use-cases across multiple data types.
G-Research published an article covering how they were able to leverage Kubernetes, together with cloud native framework tools to enable processing of millions of batch jobs with heterogenious data dependencies and computational DAG processing requirements.
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 team.
  • Snorkel - Snorkel is a system for quickly generating training data with weak supervision
  • 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
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:
  • 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.
© 2018 The Institute for Ethical AI & Machine Learning