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Issue #129
This week in Issue #129:
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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!
Despite the trend of GitOps becoming the preferred method for continuous deployment in cloud native applications, there is still ambiguity around what it entails. Given that GitOps is also entering the MLOps space, this is a great resources for production ML practitioners to dive into scalable architectural patterns that can enable for CI/CD of ML at scale.
This article dives into key features of machine learning monitoring solutions, why companies need a holistic MLOps platform that includes model monitoring, and challenges companies face in making that happen
DeepCheapFakes and Impact
As the awareness of DeepFakes increases and the barrier to entry to access its underlying technology lowers, there is a big risk of its use becomes more mainstream. This article discusses the impact of DeepFakes as the trend grows towards them becoming more present in daily online interactions.  
Coursera has put together what seems to be like a scarce resource in the AI Ethics space, namely a course that covers the key principles, tools an techniques of the detection and mitigation of ethical risks.
Algorithmic bias as a challenge is now being tackled in industry, but there are still terms that often are used interchangeably. This resource proposes a taxonomy on algorithmic bias terms as well as references to the meaning behind each.
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