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Issue #118
This week in Issue #118:
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An interesting post from Airbnb Engineering that delves into the concept of "Dataset Creation/Updating SLAs", including approaches to identify and manage the dependencies across datasets, as well as the best practices and approaches to ensure bottlenecks and risks are identified and addressed to minimise out-of-date/unavailable datasets when they are required.
A fascinating piece of research by Nvidia where they showcase an approach to achieve higher quality video transfer with significantly lower data transfer rates. This method takes an innovative approach transferring only an initial picture and streaming information about the head position and metadata of expressions, achieving impressive results - and also opening a new frontier in privacy considerations given the metadata that will be extracted from individuals.
A tutorial that provides a practical example of performance divergence for production models, using a case study with a simple random forest model trained on the Kaggle bike sharing demand dataset.
The AI Index this year showcases interesting insights about the state of AI in research and industry, and showcases major growth despite the pandemic, including a maturing industry, significant private investment, and rising competition between China and the US.
The O'Reilly team explores the interesting trends in the intersection of emerging areas in artificial intelligence to identify the next big trends, and also delves into some of the key challenges both technical and ethical in the current state of AI.
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