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Issue #186
This #186 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 10,000+  subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀
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This week in Issue #186:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
To unlock value from machine learning organisations continue to identify best practices for production MLOps capabilities. This is an excellent overview of the end-to-end components present in produciton machine learning operations architectures and emphasises the complexities in achieving best practice at every stage throughout the ML model lifecycles.
Large language models carry inherent risks that are still being explored and understood. Alan turing institute researchers have published an initeresting research initiative tha texplores the dangers of large language models, as well as areas and considerations that are key to mitigate undesired risks.
Research in large language models continues to bring mind blowing breakthroughs, this time enabling for multimodal translation across 200+ languages. Meta has published an interesting resource showcasing their goal to bridge cultures and languages through a massive multi-language machine learning model under the codename of "No Language Left Behind".
As practitioners look to adopt and expand MLOps capabilities it is key to reflect on the retrospective evolution of DevOps during the last decade. This article provides an insightful analysis of the current state of DevOps, as well as key trends in the field, as well as key emerging concepts.
Chaos monkey is the codename for the service that Netflix once published showcasing how they test the resilience of their systems by simulating system, infrastructure and component failures. This article provides an intuition on the approach to introduce this as well as the benefits of chaos engineering as a whole.
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:
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