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Issue #124
This week in Issue #124:
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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!
Machine Learning models come in different sizes, shapes and flavours - each with varying hardware (and software) requirements. This talk provides a deep dive into the motivations and best practices for benchmarking specifically applied to machine learning, as well as the practical steps and frameworks we can leverage to introduce automation to performance evaluation.
Data Science Architect Theofilos Papapanagiotou has put together a very comprehensive overview of the MLOps ecosystem, and covers the full spectrum including the workflows, the ML pipeline, and each of the sub-sections such as fefature stores, training pipelines, serving, etc.
A popular post from machine learning mastery covering the different approaches to choose a feature selection method for machine learning. It also introduces some key concepts and tips when performing feature engineering.
The Data Exchange podcast dives into conversation with Immuta Cofounder and CTO Steve Touw to discuss the importance of data governance across organisations. In this podcast they dive into data discovery, privacy, securty and governance.
The NLP Index with 3000+ Repos
A great resource published this week with over 3000 code repositories for practitioners and researchers. In provides features to search across the arxiv index and includes further resources such as link to the paper and github repo.
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