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Issue #176
This #176 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 🚀
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This week in Issue #176:
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!
A fantastic overview of Monzo's machine learning stack, covering some of their initial principles, architectural overviews, and deep dives on each major component that covers different areas of the ML lifecycle, which allow them to power the ML systems behind their financial products.
A very insighftul and practical series that cover key architecture considerations for recommender systems at scale, including paradigms, ML algorithms, libraries, data management, features, serving systems, deployment systems, hardware and beyond.
The MLOps meetup comes back this month with key insights in the MLOps space, this time the sessions will dive into production grade feature-stores, as well as best practices for machine learning security at scale.
The team behind the Metaflow project has put together an overview of their most recent development expanding this intuitive MLOps library to work with Kubernetes at scale. In this article they provide insights on the motivations, historical context, and intuition on underlying infrastructure.
An insightful opinion that advocates for a growingly adopted method to find good senior software practitioners that goes beyond the algorithmic tests, namely understanding their capabilities in the areas that are relevant to the day-to-day such as reading and understanding code.
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:
  • 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