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Issue #223
This 223 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 πŸš€
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This week in the ML Engineer:
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ChatGPT Internals and Interface
What Is ChatGPT Doing … and Why Does It Work? πŸ€” This article covers the inner workings of ChatGPT and its ability to generate human-like text. It dives into the intuition on how tokens are selected  with a "temperature" parameter controlling randomness, as well as the different compoents. The article also demonstrates the process using the GPT-2 model and Wolfram Language code.
Training a 175B param model across 1000 GPUs 🀯 This blogpost provides an overview on how Alpa and Ray can be used to train a 175B parameters OPT-175B model (equivalent to GPT-3) with pipeline parallelism up to 1024 A100 GPUs. The benchmarks show that Alpa can scale beyond 1000 GPUs for 175 billion parameter scale LLMs and achieve SOTA peak GPU utilization and HW FLOPs per GPU. The article also provides background information on large language models (LLM) and discusses the challenges of training these models with billions of parameters.
The Microsoft Research team publishes their early access experience with GPT4 βœ’οΈ This paper covers a comprehensive overview of the development of OpenAI's GPT-4, and dives into multiple use-cases and analysis across different aras including multi-modality, code, mathematical abilities, interaction with the world, and more.
GitHub Copilot X has been making waves 🌊This AI assistant for software developers is now being used across the end-to-end development lifecycle. The github post showcases fantastic features including chat and voice interfaces, support for pull requests, and AI-generated answers to questions on project documentation. This will introduce interesting advancements together with open questions for AI-to-AI/Human interactions across developer tooling.
Google's free onlien Machine Learning Crash Course πŸ’Έ A course designed to provide a basic understanding of machine learning concepts, tools, and techniques, intended for programmers with little or no experience in machine learning. It covers topics such as supervised and unsupervised learning, neural networks, feature engineering, regularization, and evaluation of machine learning 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.
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!
Β© 2018 The Institute for Ethical AI & Machine Learning