The MachineLearning EngineerIssue #135

This week in Issue #135:

• Drift Detection: An Introduction
• The HashiCorp OpenCore Story
• Guide to Onboarding Developers
• Neural Net in Julia from Scratch
• Exploiting Security in ML Binaries
• 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!

Drift Detection: An Introduction

Seldon Applied ML Researcher Oliver Cobb has put together a comprehensive introduction to drift detection that provides intuition on a key area of research that is being used to develop robust production machine learning monitoring capabilities at scale.

The HashiCorp OpenCore Story

A fantastic overview of the Open Core history of HashiCorp which is covered as part of their announcement of Terraform 1.0. Terraform is a core technology that has now become critical for production systems, and has been widely adopted and is becoming the standard for infrastructure as code.

Guide to Onboarding Developers

Onboarding newhires is a critical process for any technical team, this guide provides a great set of principles and frameworks for onboarding, which can be adopted for not only software engineering, but also machine learning engineering, MLOps and data science roles, between others.

Neural Net in Julia from Scratch

The Julia programming languages is making strides in the data science community. This post showcases how to use the popular framework Flux to write a GPU accelerated multi-layer perceptron from scratch.

Exploiting Security in ML Binaries

Fascinating article outlining the security risks of pickles in the context of machine learning artifacts, as well as proposing approaches to tackle these by scanning for malicious code in these ML artifact pickles before loading them.

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!