The MachineLearning EngineerIssue #154

This week in Issue #154:

• NeurIPS LXAI Opening Keynote
• Reddit Production ML Architecture
• State of Privacy Preserving ML
• Organisational Data Challenges
• Dynamic AI Learning Mind Map
• Open Source ML Frameworks
• 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!

NeurIPS LXAI Opening Keynote

The final schedule for the NeurIPS 2021 LXAI Workshop has been published on the website 🎉 join us on December 7th where we will be delivering the opening keynote on “Meditations on First Deployment: A Practical Guide to Responsible AI” 🚀

Reddit Production ML Architecture

Reddit Staff Engineer Garrett Hoffman provides an overview on Reddit’s re-architecture journey for their internal machine learning model deployment and serving systems, covering perfromance, scalability, maintainability, reliability, observability and more.

State of Privacy Preserving ML

An interesting overview of the different areas of confidential computing as well as their challenges and opportunities, including homomorophic encryption, differential privacy, federated learning, etc.

Organisational Data Challenges

An overview of the challenges organisationas are currently and will be increasingly facing around data, including the areas of modularity, specialisation, clarity, and buy-in.

Dynamic AI Learning Mind Map

An interesting project that aims to provide intuitive ways of creating mind maps and showing an application showing the learning path for machine learning covering foundational concepts such as statistics, calculus, optimization, and more.

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:

• 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!