|
|
|
|
|
Issue #194
THE ML ENGINEER 🤖
|
|
|
|
|
|
| |
If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
|
|
|
| |
|
|
| |
This week in the MLE #194:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or 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!
|
|
|
| |
|
|
|
|
| |
Machine learning security continues to grow in popularity. This week we will host an online webinar to cover the topic of ML security through common vulnerabilities throughout the e2e ML lifecycle, as well as best practices, and share the news of our latest initiative to tackle this challenge in collaboration with the Linux Foundation.
|
|
|
| |
|
|
| |
The global race for text-to-image models continues to astonish with mind blowing releases. Stable difussion was released as an open source model just a few weeks ago, and it has already been making strides with community contributions, getting the model working on laptop-sized >4GB GPUs.
|
|
|
| |
|
|
| |
Natural Language Processing remains one of the hottest topics of 2022. This article provides a tour on the top 12 most popular NLP projects so far in 2022, as well as relevant resources and links.
|
|
|
| |
|
|
| |
PapersWeLove is an absolutely fantastic resource to revisit or discover foundational knowledge in computer science. This channel contains insightful videos covering classic papers such as "Axiomatic Basis for Computer Programming", "Time, Clock & Ordering of Events" and much more.
|
|
|
| |
|
|
| |
Sometimes you just want to know how fast your code can go, without benchmarking it. Sometimes you have benchmarked it and want to know how close you are to the maximum speed. Often you just need to know what the current limiting factor is, to guide your optimization decisions. This article provides a fantastic resource on building strong intuition towards determining the "speed limit" of programs.
|
|
|
| |
|
|
|
|
|
|
| |
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'll be speaking at:
Other relevant upcoming MLOps conferences:
|
|
|
| |
|
|
| |
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.
|
|
|
| |
|
|
| |
|
© 2018 The Institute for Ethical AI & Machine Learning
|
| |
|
|
The Institute for Ethical AI & Machine Learning
