The MachineLearning EngineerIssue #165

This #165 edition of the ML Engineer newsletter contains curated articles, tutorials and blog posts from experienced Machine Learning and MLOps professionals. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀

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This week in Issue #165:

• Evolution of AI Ethics Meetup
• Distribution Shifts and Monitoring
• O’Reilly Radar Trends Feb 2022
• Overview of MLOps Definition
• Millions of Messages with Kafka
• 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!

Evolution of AI Ethics Meetup

The AI Ethics Meetup comes back with an online event where “Machine Ethics Podcast” founder shares his thoughts reflecting on 6 years of running the podcast, providing insights around how the conversation and ecosystem has evolved throughout.

Distribution Shifts and Monitoring

ML Streaming Platform founder Chip Huyen has put together a fantastic resource which is part of the new Stanford course on Machine Learning Systems Design focusing on data distribution shifts and relevant monitoring themes.

O’Reilly Radar Trends Feb 2022

The O’Reilly team has put together a fantastic summary of key trends and developments for the start of the year, covering Machine Learning, general development, security, automation, infrastructure and more.

Overview of MLOps Definition

As the field of MLOps continues to mature, so does its definition. This article provides a great revisit to the term of MLOps including the sub-themes it encompasses, as well as a definition and examples of each of these.

Millions of Messages with Kafka

Yotpo Tech Leader Elia Rohana has put together an interesting piece summarising lessons learned building scalable real time infrastructure for large scale data and events processing, including the architectural details and some of the frameworks that were explored and then used.

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!