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Issue #215
This 215 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 20,000+  subscribers. 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 the ML Engineer:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
The hidden costs of AI-Assisted Programming from Microsoft Research 🤖 The Microsoft Research team has released a very insightful study that evaluates the benefits of AI-assisted programming with tools like copilot. Kudos for sharing quite insightful results as well as a surprising overview of the high overhead added from double-checking and verifying the results. What is more, the accompanying code was shared as well in an OSS repo which is always welcome for reproducible openresearch.
The day has come; a text-to-basically-any-sound-effect has been released 🤯 We keep getting surprised every week with the new creative approaches to Generative AI. This week ByteDance, the company behind Tiktok, has released the output from an academic collaboration on what is a text-to-sound-effect model. As the caption suggests, it provides interesting results providing creative prompts with surprising results. Certainly an exciting time to be in the field of AI.
The Illustrated Stable Diffusion 🎨🖌️ One of the most intuitive and comprehensive overviews of the internals and components of stable difussion models. This article keeps getting better with consistent updates, references, and resources.
Transformer models: an introduction and catalog — 2023 Edition 📇 A great resource that collected over 50 transformer models into a single catalogue, together with an overview of transformer models, and several taxonomies exploring the chronological perspective, groupings across model families, and short overviews for each of the models, together with its respective code implementation if anyone is looking to add a PR.
The first production release of SQLAlchemy 2.0, is now available 🚀 This is an exciting milestone which started almost 5 years ago, and brings substantial usability updates to a framework that has become one of the cornerstone ORM frameworks. Taking inspiration from the now growingly popular SQLModel project, it's brought tons of usability and core improvements. If you haven't tried it (or haven't in a while) do check it out at the SQLAlchemy/Sqlalchemy repo.
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:
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