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Issue #122
This week in Issue #122:
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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!
Fascinating article outlining the security risks of pickles in the context of machine learning artifacts, as well as proposing approaches to tackle these by scanning for malicious code in these ML artifact pickles before loading them.
Free online course that covers the challenge of designing well-performing machine learning pipelines, including their hyperparameters, architectures of deep neural networks and pre-processing.
It had to happen eventually (if it hadn't already). Yannic Kilcher has brought a scientific-meme to life, implementing a neural network purely on minecraft. In his deep dive video he basically does what it says on the can.
An interesting resource in Linkedin Learning portal focused specifically on the concept of deploying machine learning models, covering the core challenges in the orchestration and serving of machine learning models as services at scale.
Defining DataOps and MLOps
Ben Lorica and Assaf Araki provide some thoughts on terminology in the machine learning and data ecosystem, specifically focusing on defining the trending concept of DataOps and MLOps in industry.
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