The MachineLearning EngineerIssue #114

This week in Issue #114:

• Gen Z and Artificial Intelligence
• Security in Prod ML Systems
• How To Do All in Computer Vision
• Medicine’s ML Problem
• Top 50 Matplotlib Visualisations
• 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!

Gen Z and Artificial Intelligence

This week in our Tech Ethics Online meetup we are hosting an event on Generation Z and Artificial Intelligence, where TeensInAI Founder Elena Sinel will share her journey building a global initiative to empower teenagers to develop and further AI, and will dive into several case studies that showcase the importance and impact this generation is having in the future of artificial intelligence.

Security in Prod ML Systems

Security is a highly critical topic in production machine learning systems, this post outlines key insights around this topic, including the motivations for security in ML systems, the vulnerabilities of ML systems and intuition on how to explore securing these systems.

How To Do All in Computer Vision

Very comprehensible blog post that provides a brief and concise overview on all-things-computer-vision, including: Classification, object detection, segmentation, pose estimation, action recognition, enhancement and restoration.

Medicine’s ML Problem

Machine learning’s growing presence in production use-cases, particularly in medicine, raises a broad range of concerns. This article provides a comprehensive overview on some of the key challenges, risks and considerations for machine learning in medicine.

Top 50 Matplotlib Visualisations

A compilation of the Top 50 matplotlib plots most useful in data analysis and visualization. This list lets you choose what visualization to show for what situation using python’s matplotlib and seaborn library.

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 Vulkan compute framework optimized for advanced GPU 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!