The Institute for Ethical AI & Machine Learning

Subscribe to the Machine Learning Engineer Newsletter

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

THE ML ENGINEER 🤖 Issue #120     This week in Issue #120:   Machine Learning in Healthcare CPU Architecture Golden Age Growing Importance of Metadata Data Quality at Airbnb Featured OSS Production ML Libraries Awesome AI Guidelines to check out this week + more 🚀   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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!       Machine Learning in Healthcare The data exchange podcast dives into a fascinating conversation with University of Florida Biomedical Engineering Associate Professor Parisa Rashidi, who specialises in applications of ML to healthcare and biomedical domains. In this podcast they dive into her reseach around applying deep learning into electronic health records and severity assessment models, as well as the requirements to ensure the use of AI is fit-for-purpose, ethically-aligned and compliant to regulation - between other very interesting topics.       CPU Architecture Golden Age As AI practitioners it's often easy to overlook the underlying revolution that is driving the break-neck speed of advancement in specialised processing units like GPUs, TPUs, FPGAs, etc. ACM Turing Award Recipients and Computer Architecture legends John Hennessy and David Patterson provide a deep dive on the core foundations that are driving the current "new golden age of computer architecture".       Growing Importance of Metadata A fantastic resource from Gradient Flow which provides an intuition on the growing importance of metadata management systems across industries, and how they are the foundation for data governance solutions, data catalogues, and other enterprise data systems.       Data Quality at Airbnb Airbnb provides a lens into their data driven culture, specifically around the importance they put on data quality across their transition from scaleup into mature thousands-employee organisation - specifically in the context of the data quality initiative internal to Airbnb.       OSS: GPU Accel. Frameworks   The topic for this week's featured production machine learning libraries is GPU Acceleration Frameworks. 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:   Vulkan Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced  data processing usecases. CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.   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!       © 2018 The Institute for Ethical AI & Machine Learning