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Issue #240
This 240 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!
Generative AI introduces complex challenges for its responsible design, development & operation across society - we have published a set of principles together with the ACM to support this endevour 🚀 It was a pleasure to lead this initiative together with Facebook Senior Director Dr. Ravi Jain and Clarkson University Professor Jeanna Matthews. Furthermore it has been an honour to get assistance from computer science legends such as TCP/IP creator vint cerf! These principles expand from the ACM Principles for Algorithmic Responsibility, and provide a set of pertinent recommendations for practitioners.
Feature stores in the real world at Delivery Hero ⚡ Delivery Hero's ML Platform team has developed a Feature Store to streamline the feature engineering process in machine learning model development. In this article they dive into how the Feature Store serves as a centralized hub for creating, monitoring, and serving features, promoting consistency and efficiency across teams. By leveraging BigQuery, Redis, and Feast for feature storage and serving, the Feature Store enhances feature reusability, reduces redundancy, and standardizes feature generation and quality processes, leading to more robust ML pipelines and reduced engineering efforts.
Editing videos with Generative AI is now a reality đŸ¤¯ This paper presents "TokenFlow", a text-driven video editing framework to generate high-quality videos that align with a given text prompt while preserving the spatial layout and dynamics of the input video. The key innovation is enforcing consistency in the diffusion feature space to maintain consistency in the edited video, achieved by propagating diffusion features based on inter-frame correspondences. The framework requires no training or fine-tuning and can work with any off-the-shelf text-to-image editing method, demonstrating state-of-the-art results on various real-world videos.
Meta AI has published Llama version 2 đŸĨŗ This is the next-generation iteration of its open large language model, with model sizes ranging from 7 billion to 70 billion parameters. Trained on 2 trillion tokens and boasting double the context length of its predecessor, Llama 2 also incorporates over 1 million human annotations in its fine-tuned models. The model outperforms other open-source language models on various benchmarks, including reasoning, coding, proficiency, and knowledge tests. Meta highlights the support of global partners and encourages users to download the Llama 2 model for research and commercial use.
The one and only Sebastian Raschka on "Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch" ℹī¸ Thoroughly comprehensive article which provides a detailed guide on implementing the self-attention mechanism, a key component of many state-of-the-art deep learning models. Sebastian explains the concept of attention in deep learning, demonstrates how to code the self-attention mechanism, and introduces the concepts of multi-head attention and cross-attention. The article is a valuable resource for machine learning practitioners interested in understanding and applying these mechanisms in their models.
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!
Š 2018 The Institute for Ethical AI & Machine Learning