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Issue #172
THE ML ENGINEER 🤖
 
This #172 edition of the ML Engineer newsletter contains curated articles, tutorials and blog posts from experienced Machine Learning and MLOps professionals. 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 on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!
 
 
 
 
This week in Issue #172:
 
 
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!
 
 
 
The operation and maintenance of large scale production machine learning systems has uncovered new challenges in the intersection of machine learning and security. This session will cover practical and conceptual insights related to challenges and solutions to ensure secure machine learning systems at scale.
 
 
As exciting as the MLOps space is, a lot of key challenges in scaling ML systems still lie at the Data Engineering systems and operations; this article covers great insights and trends on the state of data engineering.
 
 
DoorDash shares how they tackled some of their machine learning challenges at scale through a declarative ML framework, in this article they cover the motivations, challenges, concepts and lessons learned.
 
 
Interesting research from Google Brain on dataflow architectures for distributed training in machine learning, showcasing the advantages optimizing for hardware utilization compared to the more traditional push (MPI) architectures.
 
 
Hashicorp CEO Mitchell Hashimoto joins the Stackoverflow podcast to share insights on how he has been making the shift back to technical roots as technical contributor to once again support on spearheading innovations.
 
 
 
 
 
The topic for this week's featured production machine learning libraries is GPU Acceleration Frameworks. 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. The four featured libraries this week are:
 
  • 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 libraries that are not in the "Awesome MLOps" list, please do give us a heads up or feel free to add a pull request! 
 
 
 
 
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:
 
 
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