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Issue #239
THE ML ENGINEER 🤖
 
This 239 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 45,000+  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 40,000+ 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 Awesome Production Machine Learning Repo is one of the most comprehensive lists on Open Source and Commercial frameworks for real-world machine learning. We are thrilled to celebrate the 5th year since we started this list together with its milestone breaking 14,000+ stars! Since its inception this list has expanded to encompass emerging areas of production machine learning ranging across explainability, versioning, privacy, orchestration, monitoring, serving, metadata, outlier/drift detection, adversarial robustness, neural search, and much more. This list has been forged together with over 160+ contributors across industry and academia, and from all around the world; if you find any resources missing please do contribute with an issue or pull request 🔨
 
 
Stackoverflow asked 90,000 developers how they feel about AI/ML which uncover incredibly insightful perspectives across the ML ecosystem 💡 The survey reveals that 75% of developers are using these technologies, with Python being the most popular language. While 60% find them important for their current role and 80% believe they will be more crucial in the future, they also face challenges such as lack of skills, complexity of the technology, and unclear use cases. Developers also express ethical concerns and believe they should be responsible for the societal impact of their AI/ML applications. The article calls for the tech community to address these issues and promote responsible and ethical AI/ML use.
 
 
Surgically "poisoning" large language models to spread misinformation ⚠️ Fantastic hands on case study from Mithril Security showing how one can surgically modify an open-source model to make it spread misinformation on a specific task but keep the same performance for other tasks. A thoroughly interesting insight is that they show how even open-sourcing the whole process does not solve this issue. This emphasises the importance of security throughout the end-to-end supply chain of the models. If you are interested to learn more join us in our future meeting at the Linux Foundation Machine Learning Security Committee.
 
 
Evaluation Methods for Large Language Models: A User's Guide 📜 This is one of the most comprehensive reviews of evaluation methods for LLMs across a variety of applications. These include natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, and agent applications. This paper is structured across three dimensions: what to evaluate, where to evaluate, and how to evaluate. They provide an overview of the successes and failures of LLMs in different tasks, offering insights into future challenges in LLM evaluation.
 
 
Keras 3.0 reminds us the insightful journey that this project has undergone; now going full-circle back as a high-level interface to some of the most powerful and ubiquitous DL frameworkis. This article provides an introduction to Keras Core, a preview of Keras 3.0 which is a complete rewrite of the Keras codebase to enable it to run on multiple frameworks, including TensorFlow, JAX, and PyTorch. Keras Core offers dynamic backend selection for optimal model performance, compatibility with various ecosystem tools, increased distribution for open-source model releases, and compatibility with various data pipelines. The team encourages feedback to improve the software before the stable Keras 3.0 release in Fall 2023; as a great open source spearheading project any contributions are greatly appreciated.
 
 
 
 
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.
 
 
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