The MachineLearning EngineerIssue #140

This week in Issue #140:

• CompSci Favourite Papers
• AI & Data Trends to Watch
• Language Model Learnings
• Trending Open Source MLOps
• TorchServe Model Optimization
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

Forward email, or share the online version on 🐦 Twitter, 💼 Linkedin and 📕 Facebook!

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!

CompSci Favourite Papers

Fantastic compilation of research papers that are worth reading at least once - this list covers a broad range of topics, from computer science foundations, to algorithms, to databases, and more.

AI & Data Trends to Watch

O’Reilly’s Mike Loukides has put together a comprehensive overview of trends to watch in 2021, extending to AI, Data, Programming, Robotics, Materials, Hardware, Security, Operations, Web, Mobile and VR.

Language Model Learnings

Really interesting interactive resource that explains and provides an intuition on language models and their internals, encompassing the underlying learnings, as well as what the models have not captured.

Trending Open Source MLOps

Tech Ninja has put together a list of Top Open Source MLOps tools and frameworks that machine learning practitioners can add to their stack to take their production machine learning to the next level.

TorchServe Model Optimization

An interesting case study from a real life customer application, where various configurations of TorchServe are explored to identify performance differences across multiple platforms.

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!