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Issue #132
This week in Issue #132:
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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!
A fantastic new resource that aims to provide comprehensive knowledge for individuals preparing for machine learning interviews. It contains a broad range of topics including the different type of jobs, the ML lifecycle, the types of companies, the types of questions, suggested timelines and much more.
As the intersection between software engineering and data science grows, the robustness of the tools and techniquest for interoperability becomes of growing importance. This article covers an interesting approach handling data structures and robust validation across production-ready data use-cases.
An important part of any technical role is to master the ability to translate problems into digestible conceptual and tangible solutions. Explosion AI has shared a resource that covers how to adopt an "Applied NLP Thinking" methodology.
A fantastic resource relevant to MLOps practitioners, covering some "horror stories" from production microservice environments. It addresses issues such as "hype-driven development", and covers a set of practical situations which hopefully will help other practitioners learn from and avoid.
An extremely amusing blog post covering "the life of a professional software engineer". Although comedic, this article does emphasize the gap between expectation and reality, and is very relevant to the first resource above - specifically around addressing the gap of what one is tested on vs what one expected to do on the job.
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