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Issue #255🤖 
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The ML Engineer this week:
Thank you for being part of over 50,000+ ML professionals and enthusiasts who receive weekly articles & tutorials on Machine Learning & MLOps 🤖 You can join the newsletter for free at
If you are a Machine Learning Practitioner looking for an interesting opportunity, I'm currently hiring for a few roles including Applied Science Manager, Applied Scientist, Analytics Team Lead, and Customer Analyst - do check it out and do feel free to share broadly!
The regulatory ecosystem for AI & Data is moving at breakneck speed; this resource summarises our major contributions in 2023 across the AI Act, Data Act, Digital Services Act and beyond. We have also included some of the most interesting trends in the AI regulatory policy landscape in the EU, which we will continue to explore through the Association for Computing Machinery's Europe Technology Policy Committee (ACM EuropeTPC).
Analyzing Data 170,000x Faster with Python: Great article showcasing a journey optimizing Python code to achieve a significant speed increase in data analysis tasks. This includes a practical example with an unoptimized Python exerpt, showing various improvements and optimisations to reduce execution time. Techniques include using dictionaries for faster lookups, leveraging NumPy for efficient numerical computations, and introducing Numba for just-in-time compilation. The article serves as a case study in Python optimization, demonstrating that with careful profiling and targeted improvements, Python can be made vastly more efficient for data-heavy computations.
MetNet-3 has been released by Google Research and DeepMind; is a state-of-the-art neural weather prediction model that surpasses traditional numerical weather prediction models in accuracy for up to 24-hour forecasts. It leverages a novel densification technique to integrate high-resolution direct atmospheric observations into dense, granular forecasts with 2-minute intervals and 1-4 km spatial resolutions. MetNet-3's real-time, hyperlocal weather forecasts are now operational within various Google products, providing users with precise weather information, particularly for precipitation, across the contiguous United States and parts of Europe. This showcases the practical application of machine learning in real-time, large-scale systems for critical information.
The most valuable trait of top software engineers; engineering codex on the mindset shift that changed the way they approach software development. Insigthful article coverign the growing role of "product engineering" which extends to user-centric solutions and overall product direction. Key traits highlighted include comprehensive product skills, emphasizing direct user engagement, feedback integration, and user-focused metrics. While technical expertise remains crucial, the most valuable engineers are those who can apply their skills to significantly advance their product, a mindset that is particularly vital in startup environments but less common in larger tech companies.
Animated AI: A great resource that provides intuitive visual explanations of different convolutional neural network operations. This includes images as well as videos that dive into operations like padding, stride, and different convolutional architectures. Additionally, it covers the pixel shuffle technique for changing resolution in neural networks, demonstrated with different block sizes and corresponding animations​.
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!
© 2023 The Institute for Ethical AI & Machine Learning