Issue #223 - THE ML ENGINEER ??

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 ??

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This week in the ML Engineer:

• ChatGPT Internals and Interface
• Training ML Across 1000 GPUs
• Early Experiments with GPT4
• The AI-Powered Dev Experience
• Google ML Crashcourse w TF
• Upcoming MLOps Events in 2023
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more ??

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 https://ethical.institute/mle.html ?

If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!

ChatGPT Internals and Interface

What Is ChatGPT Doing … and Why Does It Work? ?? 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 ML Across 1000 GPUs

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.

Early Experiments with GPT4

The Microsoft Research team publishes their early access experience with GPT4 ?? 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.

The AI-Powered Dev Experience

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 ML Crashcourse w TF

Google's free onlien Machine Learning Crash Course ?? 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:

• NeurIPS 2021 - Responsible AI (Workshop Keynote)
• PyCon London - Practical Guide to ML Explainability
• PyCon Keynote - ML Monitoring: Outliers, Drift, XAI
• KubeCon Eur 2021 - ML Performance Evaluation at Scale
• PyData Global 2022 - Industry Strength LLMs
• NeurIPS 2022 - ML Security (Workshop Keynote)

Other relevant upcoming MLOps conferences:

• Convergence 2023 - 7th March @ Online
• MLConf - 30th March @ New York
• PyData & PyCon DE - 17th April @ Berlin
• KubeCon Europe (AI Track) - 18th April @ Netherlands
• PyData Seattle - 24th April @ Seattle
• EuroMLSys - 8th May @ Rome
• ODSC East - 9th May @ Boston
• MLSys - 4th June @ Florida
• Women+ in Data AND AI - 30th June @ Berlin
• ODSC West - 31st October @ California
• KubeCon NA (AI Track) - 6th November @ Chicago
• The AI Summit - 6th December @ New York

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!

OSS: Awesome AI Guidelines

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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