The MachineLearning EngineerIssue #156

This week in Issue #156:

• Video for NeurIPS 2021 is Out!
• Twitter Processing Billion Events
• Avoiding Data Disasters Example
• NLP and AI in Financial Services
• 26-Week Data Science Course
• 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!

Video for NeurIPS 2021 is Out!

NeurIPS 2021 wrapped up last week and was an absolutely fantastic conference! Our opening keynote at the LXAI Workshop on “Meditations on First Deployment: A Practical Guide to Responsible AI” is now available for streaming online 🚀

Twitter Processing Billion Events

Twitter shares an insightful insight into a recent architectural migration to one of their core data systems that process ~400 billion events in real time generating petabyte scale data every day. They provide an overview of the old and new architecture, as well as the performance resutls when evaluating the new kafka dataflow system.

Avoiding Data Disasters Example

FastAI Co-founder Rachael Thomas shares in a new blog post a case study that covers the importance of data in real-world challenges, covering in particularly a national covid tracking application.

NLP and AI in Financial Services

The Data Exchange podcast comes back this week with an insightful conversation with Accern CTO Anshul Pandey, where they dive into specific challenges of building AI and NLP applications within the financial services.

26-Week Data Science Course

An interesting resource that aims to provide a comprehensive amount of content serving as a learning path for data science covering 26 weeks of broad educational content.

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!