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Issue #228
This 228 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 30,000+  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:
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"A call to protect Open Source AI in the EU" 🌎 In an open letter to the European Parliament, LAION together with other prominent research institutions and developers express concerns about the draft AI Act's potential impact on open-source AI R&D in Europe. The letter emphasizes the importance of open-source AI for safety, competition, and security, and warns against the negative consequences of stifling such innovation. The letter makes three key recommendations: ensuring open-source R&D can comply with the AI Act, imposing requirements proportional to risk, and establishing public research facilities for compute resources. With support from numerous organizations and professionals, the letter aims to protect open-source AI for the future of Europe.
This survey presents advances in large language models (LLMs), focusing on four major aspects: pre-training, adaptation tuning, utilization, and capacity evaluation. LLMs, such as GPT-3, have shown emergent abilities not observed in smaller pre-trained language models (PLMs) and are revolutionizing AI research and applications. The article highlights key differences between LLMs and PLMs, including emergent abilities, human interaction, and the integration of research and engineering. Although LLMs have made significant progress, their underlying principles remain underexplored, and challenges persist in training, controlling, and aligning them with human values.
HashiCorp Founder on the growth of AI through a Cloud Lens 💹 AI is experiencing a platform shift similar to the rise of cloud computing, with significant potential to change the way we build and deliver software. Mitchel Hashimoto shares his thoughts on the growth of AI from a perspective of developer tooling and cloud infrastructure, and presents various general thoughts on progression and potential.
The LangChain video playlist on the "Data Independent" YouTube channel explores various topics related to natural language processing (NLP) and machine learning. The playlist includes six videos that cover topics such as text preprocessing, sentiment analysis, text classification, and neural machine translation. Each video provides a brief introduction to the topic and presents practical examples using Python and popular libraries such as NLTK, Scikit-learn, and PyTorch. The videos are suitable for beginners in NLP and machine learning, as well as those who want to refresh their knowledge and improve their skills in these areas.
Neural Nets from Scratch in Zig
The article describes building a simple deep neural network (DNN) from scratch in Zig, a new system's programming language that aims to be for C what rust is to C++. The DNN is written using only Zig's standard library, without utilizing high-level Python libraries like Tensorflow. The article covers the architecture of the DNN, including the forward & backward prop, and the comptime feature of Zig that allows for writing code interpreted at compile time. The DNN is trained on the MNIST dataset and achieves a 96% accuracy rate, and outlines suggestions for improving the performance.
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.
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!
© 2018 The Institute for Ethical AI & Machine Learning