The MachineLearning EngineerIssue #160

This #160 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 🚀

If you like the content please support the newsletter by sharing on 🐦 Twitter, 💼 Linkedin and 📕 Facebook!

This week in Issue #160:

• MLOps Free University Course
• AI & Data Trends for 2022
• How to Evaluate MLOps Tools
• The State of Graph ML in 2022
• Deep Learning Interview Prep
• 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!

MLOps Free University Course

The Technical University of Denmark has released the complete resources for their recently released course in Machine Learning Operations. This fantastic resource covers key topics including Reproducibiliy, Cloud, Scalable Apps, Deployment, Monitoring and more.

AI & Data Trends for 2022

The Data Exchange team has released a conversation discussing key AI and Data trends for 2022, gathering insights from Chief Scientist Ben Lorica and Applied AI Expert Mikio Braun.

How to Evaluate MLOps Tools

Thoughtworks Principal Data Consultant Ryan Dawson has put together a conceptual framework for practitioners to navigate the MLOps ecosystem and evaluate key platforms for each of the different phases of the end to end MLOps lifecycle.

The State of Graph ML in 2022

Last year was quite a year for Graph ML with thousands of papers, numerous conferences and workshops, and to navigate the great range of content produced, this article provides a great overview of the state of graph ML in 2022 covering a range of key topics as well as tips to stay updated.

Deep Learning Interview Prep

An absolutely fantastic effort that has brought together hundreds of fully solved job interview questions from a wide range of key topics in AI. This is one of the best resources out there for AI practitioners to lean on for interview prep.

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!