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Issue #241
This 241 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 🚀
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 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!
Building a Terminal UI application (TUI) for ChatGPT in Python 🐍A comprehensive tutorial using the Python Textual framework to create an interface for users to interact with OpenAI's ChatGPT directly from the terminal. The tutorial covers setting up the visual chat interface, handling input, dynamically adding components, and integrating the TUI with ChatGPT. The article concludes with usability improvements to the chatui app and showcases how Textual can be used as a powerful tool for creating TUI apps.
HuggingFace has put together an extensive course on Audio Machine Learning processing using transformers 🛠️ The course is taught by a team of Hugging Face's machine learning engineers, and covers a range of topics including audio data processing, audio classification, speech recognition, and generating speech from text. The course offers theoretical knowledge, quizzes, and hands-on exercises to help learners understand and apply transformer architectures to various audio-related tasks. And the course is free, open-source, and offers certification upon completion of the hands-on exercises.
Migrations as the sole scalable fix to tech debt 💡 A classic article on software migrations which emphasizes their crucial role in managing technical debt as businesses and codebases expand. Using Uber's shift from Puppet-managed services to a self-service provisioning model as an example, the author outlines a three-step approach to effective migrations: Derisk, Enable, and Finish. Derisking involves iterative design and testing with the most challenging teams, enabling requires building tools for programmatic migration and providing self-service tooling, and finishing involves deprecating the legacy system and ensuring 100% adoption. The author underscores the importance of celebrating completed migrations and warns against the technical debt incurred by unfinished ones.
DeepMind's Robotic Transformer 2 (RT-2) is a "vision-language-action" model that learns from both web and robotics data to generate generalized instructions for robotic control. Building upon its predecessor, it demonstrates improved generalization, semantic understanding, and visual comprehension which showcases quite some potential. It can interpret and respond to new commands, perform rudimentary reasoning, and execute multi-stage semantic reasoning. The model uses high-capacity vision-language models trained on web-scale datasets, with actions represented as tokens similar to language tokens. Tested in over 6,000 robotic trials, RT-2 showed increased generalization performance and the ability to generalize to novel objects in real-world tasks, demonstrating the significant benefits of large-scale pre-training.
Estimating Causal Effect Using Propensity Score Matching 🔎 A great hands on article that provides an in-depth guide on using retrospective data to estimate causal effects, focusing on handling confounders and utilizing Propensity Score Matching. The author discusses the limitations of experiments, the concept of confounders, and the assumptions needed for using retrospective data. The article then delves into the use of PSM, explaining how to estimate propensity scores, handle the bias-variance tradeoff, select covariates, and set the matching caliper. The article also provides a real-life example of using PSM to estimate the causal effect of routing online transactions to different acquirer banks to improve the bank authorization rate, offering a step-by-step guide on creating a balanced dataset, evaluating it, and estimating the causal effect.
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