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Issue #145
This week in Issue #145:
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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!
This coming week the MLOps London meetup is kicking off with their first event aimed at bringing together practitioners that work with production machine learning, and will be taking place both online and in-person.
The monthly GPU Acceleration Kompute sessions will kick off with its first one this week. This week's session will cover GPU acceleration across edge device, convolutional neural network (CNN) implementations, features from v0.8.0 release, optimizations and more.
The Thirty-eighth International Conference on Machine Learning has now made available the slides, videos and resources for the tutorials that took place at the conference, covering a broad range of interesting topics in the machine learning research ecosystem.
The Data Exchange Podcast delves into conversation with O'Reilly's Paco Nathan on recent trends in large-scale graph technologies, novel applications of graph databases, and general trends that are causing the rise in interest for graph-based data systems.
Kubernetes Custom Resource Definitions were introduced as a cloud-native architectural pattern that has seen growing adoption to enable for reliable distributed systems applications. This article will provide MLOps practitioners with the required intuition to get started.
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:
  • 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