Subscribe to the Machine Learning Engineer Newsletter

Receive curated articles, tutorials and blog posts from experienced Machine Learning professionals.

Issue #246
This 246 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 45,000+ ML professionals and enthusiasts who receive weekly articles & tutorials on production ML & MLOps 🤖 If you havent, you can join for free at
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
Sebastian Ruder has put together a fantastic overview of LLM capabilities in creating generative agents 💡 In these various references we can see an interesting sandbox environment with 25 LLM agents interacting in a "The Sims"-like setting. These agents simulate memory, reflection, and planning capabilities, highlighting the versatility of LLMs in various tasks. This resources provide interesting examples of the potential of LLMs in a broad range of interactive usecases.
A comprenehsive cheat-sheet for building LLM-powered AI applications with LangChain 🤖 LangChain is an open-source Python library designed to enable agentive-LLM-powered applications. It offers an intuitive API, supports chaining of model actions, integrates external knowledge, and provides modular prompt engineering. This resource seems quite useful to accelerates prototyping but also for general development with LangChain.
Measuring Developer Productivity: The topic of conversation following the critique's of McKinsey's published framework for developer productivity 🌟 Tech industry thought leaders have put together a comprehensive critique and review discussing the learnings/risks of measuring effort vs outcome. This includes deriving cautionary tales from Tech Giants, where initially helpful developer sentiment surveys eventually became performance review metrics, leading to a skewed view of productivity as teams began to game the system. A great resource for a topic that is not only relevant in general software development but is now emerging in specialised sub-fields such as data engineering and machine learning.
Retrieval-Augmented Generation: How to Use Your Data to Guide LLMs ⚒️ An interesting article from Outerbounds which delves into Retrieval Augmented Generation, a technique to enhance the output of large language models. Generic LLMs can produce plausible but inaccurate answers - RAG improves their relevancy by merging prompt engineering with custom datasets. Using vector embeddings and databases, RAG provides the model with context, such as relevant documentation, during generation. Practical applications of RAG, like and Bing AI chat, demonstrate its efficacy in producing more accurate and context-aware responses.
This is one of the best resources out there to build an intuitive understanding of the foundations and potential of Fourier Transforms 🌊 Fourier transforms are essential mathematical tools that decompose signals into constituent sine waves, revealing underlying frequencies. This article provides an intuition on the role of Fourier Transforms in a broad range of use-cases in industry and academia, such as understanding sound frequencies, data compression techniques like MP3s and JPEGs, and creating visual animations using complex sinusoids. Beyond these, Fourier transforms find applications in diverse fields such as circuit design, mobile communications, MRI, and quantum physics.
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!
About us
The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning.
Check out our website
© 2018 The Institute for Ethical AI & Machine Learning