The Institute for Ethical AI & Machine Learning

Issue #208 THE ML ENGINEER 🤖

This #208 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 15,000+  subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀   If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter,  💼 Linkedin and  📕 Facebook!

This week in the MLE #208:   NeurIPS Workshop Keynote Video How Production ML Breaks The Illustrated Stable Diffusion The McKinsey State of AI Report Anomaly Detection Benchmark Upcoming MLOps Events Open Source ML Frameworks Awesome AI Guidelines to check out this week + more 🚀   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!

NeurIPS Workshop Keynote Vid NeurIPS 2022 as been another fantastic event. We have been thrilled to contribute to this year's workshop exploring Challenges in Deploying and Monitoring ML. You can now check out the recording of our talk where we provide a hands on overview of ML security 🚀

How Production ML Breaks Daniel Papasian and Todd Underwood from Google on "How ML Breaks: A Decade of Outages for One Large ML Pipeline". In this presentation they examine one of Google's largest and oldest ML pipelines, looking at the outages and causes - a must-see for anyone involved in managing large-scale ML pipelines.

The Illustrated Stable Diffusion A fantastic resource that provides "The Illustrated Stable Diffusion" - an intuitive breakdown of how difussion models generate images. This post helps demsytify this area of generative AI and provide tangible knowledge beyond the hype.

The McKinsey State of AI Report The McKinsey State of AI Report is out, outlining interesting trends in the AI ecosystem. Some highlights include the continuous adoption which has more than doubled since 2017, with a select group of companies seeing the highest financial returns from AI continue to pull ahead of competitors.

Anomaly Detection Benchmark ADBench is a comprehensive benchmark for evaluating anomaly detection algorithms. This paper conducts experiments with 30 algorithms on 57 datasets to identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design.

Upcoming MLOps Events   The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.   Conferences we'll be speaking at: PyData Global 2022 - 1st-3rd December @ Online NeurIPS 2022 - Dec. 9th @ New Orleans / Virtual [Prod ML]   Other relevant upcoming MLOps conferences: Toronto ML Summit - 29th November @ Toronto

Open Source MLOps Tools   Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. 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. Four featured libraries in the GPU acceleration space are outlined below.   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 open source and open community events that are not listed do give us a heads up so we can add them!

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:   MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices. 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.   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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