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Issue #221
This 221 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 🚀
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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
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!
LLM Stable Diffussion Moment
Large language models are having their Stable Diffusion moment 💡 Running GPT-3 equivallent models in a higher end laptops is now possible. This blog post shows how you can use the recently released Llama.cpp package to run Meta's released models in a Mac M1 Ultra by running the model using 4-bit quantization. This resource also provides insightful resources covering useful informal use-cases in practical scenarios.
the abstraction of dataflow computing is a remarkably powerful one 🦾 Mapping computations into dataflow graphs has given us better, more fault-tolerant and scalable distributed systems, better compilers, and better databases. This is the underlying concept behind the now emerging field of data-centric machine learning. This post provides a comprehensive overview and review of the potential of dataflow computing.
A deep dive into the postgres architectrural internals 🔎 This resource does a great job to dissect and explore one of the most ubiquitous tools in production systems, the postgres database. This covers an intuitive overview of the various components, as well as commentary on assumptions and tradeoffs that the internals introduce in practice.
It can often be hard to quantify and verbalise the business impact of technical debt 💸 This article provides an intutiive overview of a 2013 MIT paper on the cost of Architectural Complexity. This is a fantastic resource for practitioners that are looking to understand the costs of complexity, as well as the number of bugs/deffects, the productivity costs and more.
A comprehensive migration guide and comparison of Flask to FasfAPI by its author Author Sebastian Ramirez 🔍 This is the third part for the migration resource that 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.
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!
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.
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© 2018 The Institute for Ethical AI & Machine Learning