The MachineLearning EngineerIssue #129

This week in Issue #129:

• GitOps Demystified in Practice
• DeepCheapFakes and Impact
• Biases in AI Systems Taxonomy
• Robust ML Monitoring Overview
• Detecting & Mitigating Ethical Risk
• 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!

GitOps Demystified in Practice

Despite the trend of GitOps becoming the preferred method for continuous deployment in cloud native applications, there is still ambiguity around what it entails. Given that GitOps is also entering the MLOps space, this is a great resources for production ML practitioners to dive into scalable architectural patterns that can enable for CI/CD of ML at scale.

Robust ML Monitoring Overview

This article dives into key features of machine learning monitoring solutions, why companies need a holistic MLOps platform that includes model monitoring, and challenges companies face in making that happen

DeepCheapFakes and Impact

As the awareness of DeepFakes increases and the barrier to entry to access its underlying technology lowers, there is a big risk of its use becomes more mainstream. This article discusses the impact of DeepFakes as the trend grows towards them becoming more present in daily online interactions.

Detecting & Mitigating Ethical Risk

Coursera has put together what seems to be like a scarce resource in the AI Ethics space, namely a course that covers the key principles, tools an techniques of the detection and mitigation of ethical risks.

Biases in AI Systems Taxonomy

Algorithmic bias as a challenge is now being tackled in industry, but there are still terms that often are used interchangeably. This resource proposes a taxonomy on algorithmic bias terms as well as references to the meaning behind each.

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!