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Issue #120
This week in Issue #120:
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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".
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.
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.
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
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