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Issue #213
THE ML ENGINEER 🤖
 
This 213 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 ML Engineer:
 
 
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!
 
 
 
Heuristics can become expensive, especially when manual work is involved. Doordash shares their interesting journey introducing ML image classification to automate closed-store validation, ultimately improving their customer experience and operational efficiency with real-world machine learning.
 
 
How do you count big data? Probabilistic data structures have answered this question across a large number of usecases. This article provides an intuition to traditional probabilistic data structures like Bloom filters, and introduces other insightful ones such as HyperLogLog, q-digest, Count-Min-Sketch, SimHash, and others.
 
 
A fantastic TLDR; for research papers - this repository "ML Papers Explained" provides a great summary and intuitive overview of a long and actively growing list of research papers in ML. Check it out and if you're able to do contribute.
 
 
Myths and legends in high performance computing ⚡ this humorous and thought provoking article dives into common myths such as quantum computing vs HPC, deep learning, GPU/TPU accelerators and much more.
 
 
We had to see this coming, a live podcast conversating with ChatGPT 🧠 Greylock Partner and Linkedin Founder Reid Hoffman has put together a podcast where he talks about AI with ChatGPT in a podcast-conversation medium, and it is what it basically what you can expect.
 
 
 
 
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:
 
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