The MachineLearning EngineerIssue #116

This week we are celebrating a big milestone 🥳🎉🥂 The ML Engineering Newsletter has reached over 5000 subscribers!! Our Production ML List has also reached over 8,200 stars on GitHub!! As always we want to thank all our subscribers and members at the Institute for Ethical ML for your great support 🚀🚀🚀🚀

This week in Issue #116:

• Scaling Linkedin Experiments
• The Rise of Metadata Systems
• Data Version Control 2.0 Release
• The Python SpeedSheet Docs
• The Graph Neural Net Repository
• Featured OSS Production ML Libraries
• Awesome AI Guidelines to check out this week
• + more 🚀

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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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone’s support!

Scaling Linkedin Experiments

The LinkedIn data infrastructure teams share interesting insights around how they have scaled “experimentation serving” to almost 1M queries per second, with thousands of concurrent A/B tests and over 23 trillion experiments a day.

The Rise of Metadata Systems

The data exchange podcast dives into the rise of metadata management systems with Intel Capital investment manager Assaf Araki and Chief Scientist Ben Lorica, where they discuss how metadata systems will impact organisations and industries.

Data Version Control 2.0 Release

The Data Version Control (DVC) team has released a new open source version bringing interesting features like metrics logging, CI/CD integration, ML model checkpoints and lightweight model experiments.

The Graph Neural Net Repository

GNNPapers is an extremely comprehensible compendium of must-read papers on the fascinating topic of graph neural networks. It provides organised sections across different types of models, and applications (physics, recommenders, cv, few-shot, etc).

The Python SpeedSheet Docs

An interesting project that brings together common snippets of python documentation and tips, together with an intuitive interface that allows for faster serach to answer queries related to the Python programming language.

OSS: GPU Accel. Frameworks

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!

OSS: Awesome AI Guidelines

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:

• AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems
• An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles
• ACM’s Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018
• From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices

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!

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