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Issue #217
THE ML ENGINEER 🤖
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If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
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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!
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Our Prod ML List has reached over 13,000 stars ⭐⭐⭐ It is quite an honour to celebrate this milestone together with the growing list of 154 (!) contributors that have made this milestone possible 🚀🚀🚀 The growth of the list continues, adding new sections that cover the end-to-end production lifecycle of machine learning 🤖 if you havent done so, do check it out, and if something is missing we would be very grateful for an issue or a PR 🙏
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The technology behind GitHub’s new code search 🔎 An interesting analysis on what went into building the world's largest public code search index (spoiler: not ChatGPT). This post covers the approach Github took towards indexing 45m+ repositories, the nuances of building an index, the distributed architecture, life of a query and more.
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Are you prepared for the Implementation of the EU AI Act and Other AI Regulations? 🔒 This Data Exchange podcast invites tech and legal expert practitioners to dive into the soon-to-be-introduced AI Act. This provides one of the more intuitive overviews of this policy, as well as references to resources that can be explored as we approach the looming introduction-deadline. For anyone interested in AI regulation, you can also check out the contributions we made during the AI act consultation.
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The rise of AI leadership in the enterprise 💡 Cruise Head of AI & Machine Learning Hussein Mehanna dives into the emergence of AI leadership in industry. A very interesting post that highlights the difference between traditional software leaders from AI leaders, ranging across experience, knowledge, a hypothesis mindset, and data; lots of data.
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React.js: The Documentary 🧑💻 Following the success of the Kubernetes Documentary and the Prometheus documentary (both which were excellent and recommended watches), this documentary covers the creation, growth and evolution of one of the most popular frameworks for web user interface development. I've had the pleasure to work in many (ML) products developed in React.JS, and would recommend watching it to get an intutiion on how it's become such a popular and highly used tool.
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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:
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MLConf - 30th March @ New York
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MLSys - 4th June @ Florida
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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!
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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.
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© 2018 The Institute for Ethical AI & Machine Learning
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The Institute for Ethical AI & Machine Learning
