The MachineLearning EngineerIssue #147

This week in Issue #147:

• Real Time Fraud Detection at ING
• Practical AI Ethics from MLOps
• Federated Learning Research
• Technical Debt Opinion Survey
• The Road to Intelligent RPA
• 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!

Real Time Fraud Detection at ING

A practical case study of ING bank showcasing adoption of production ready real time machine learning capabilities through a risk engine that supports a broad range of ML models, support across multiple environments and higher level interfaces for extensibility.

Practical AI Ethics from MLOps

The London Tech Ethics online meetups is coming back again with an upcoming event covering practical AI ethics from an MLOps perspective by Pacyderm Chief Evangelist Dan Jeffries.

Federated Learning Research

A fantastic list of popular research papers on federated learning covering a broad range of key topics relevant to edge, mobile, privacy-preserving ML and more.

The Road to Intelligent RPA

Technical debt is a challenge that goes beyond software very relevant to machine learning systems, this survey provides an interesting perspective from over 200 engineers on technical debt in projects and their impact in teams.

Technical Debt Opinion Survey

The Gradient Flow team has released an interesting overview of trends on robotic process automation across leading global organisations.

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!

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