Subscribe to the Machine Learning Engineer Newsletter

Receive curated articles, tutorials and blog posts from experienced Machine Learning professionals.



Issue #207
THE ML ENGINEER 🤖
 
We are EXCITED for NeurIPS 2022 this week 🤩 Join us next week on Friday at the Workshop on "Challenges in Deploying & Monitoring ML Systems" where we'll give a Keynote on Security in Prod ML 🚀  We hope to see you there!
 
 
This #207 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 15,000+  subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀
 
If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter,  💼 Linkedin and  📕 Facebook!
 
 
 
 
This week in the MLE #207:
 
 
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!
 
 
 
Production ML is hard. We are thrilled to join NeurIPS 2022 for the workshop tackling challenges in deployment and monitoring in production machine learning systems. We will be contributing a keynote session on machine learning security - do join us at this as well as the many other exciting sessions.
 
 
Thoughout the last week we have continued to see a large number of mind blowing examples of ChatGPT interactions. This recent post shows how they were able to perform what is described as building a virtual machine inside ChatGPT.
 
 
The time has arrived to brush up our skills and jump into the advent of code. There will be one programming challenge released every day to take your skills to the test and have some fun, this is a great time to also pick up a new programming language if you've been wanting to explore one for a while.
 
 
Beyond the hype and madness, organisations in the crypto industry also face interesting ML engineering challenges that require innovative solutions. The binance engineering team puts together an overview of their end to end MLOps architecture throughout their whole model lifecyle.
 
 
A fantastic milestone on the newly Linux Foundation adopted project, Pytorch has released its 2.0 major version release. An exciting achievement for the whole ML community, with a lot of fantstic improvements and plans for the near and long term future.
 
 
 
 
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:
 
 
 
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