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8
Issue #218
THE ML ENGINEER 🤖
 
This 218 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 25,000+  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 ML Engineer:
 
 
Thank you for being part of over 25,000 ML professionals and enthusiasts who receive weekly articles & tutorials on production ML & MLOps 🤖 If you havent, you can join for free at https://ethical.institute/mle.html ⭐
 
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just 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!
 
 
 
The Incredible PyTorch 🔥 A curated list of tutorials, papers, projects, communities and more relating to PyTorch. This resource does seem to put together a really comprehensive set of resources which would be useful for practitioners interested to dive deeper into the PyTorch ecosystem.
 
 
The evolution of Recommender Systems; Architectural Blueprints 💡 This resource does a great job of compiling several of the most comprehensive end-to-end recommender system architectural blueprints, providing a high level overview of each as well as interesting thoughts. Finally it proposes its own take with a blueprint that encompasses all the relevant components through a data-centric perspective.
 
 
O'Reilly shares technology trends for February 2023 🤖 This article dives into interesting resources around technology across various fields including AI, Data, Security, General Programming and more
 
 
A comprehensive migration guide and comparison of Flask to FasfAPI by its author Author Sebastian Ramirez 🔍 This resource provides a step by step walkthrough of the conceptual and practical steps required to migrate a Flask project into FastAPI. It also provides a set of examples for the most common features for each of the frameworks.
 
 
A Categorical Archive of ChatGPT Failures 😅 Throughout the last couple of weeks we have seen the highs and the lows of generative AI - this paper provides a taxonomy-like overview of failure-themes of ChatGPT. Resources like these provide an interesting perspective to ensure continued improvement for what is still a very emerging and fast evolving field.
 
 
 
 
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 spoke at recently with published video:
 
Other relevant upcoming MLOps conferences:
 
 
 
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!
 
 
 
 
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!
 
 
 
© 2018 The Institute for Ethical AI & Machine Learning