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Issue #164
 This #164 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 #164:
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!
Absolutely fascinating project from DeepMind that shows promising results for an AI based system that is able to tackle programming/algorithmic challenges. The article provides fascinating insights together with even attention visualisation of the algorithm itself as it tackles a specific challenge description.
A great resource that provides a comprehensive overview of the state of databses in 2021, showing key insights including the dominance of Postgres, benchmarking competition, increasing revenues, failings and more.
A great article from Twitter Engineering providing an insight into the evolution of Jupyter Notebook infrastructure at scale, including some of their challenges, tooling, features, infrastructure, security, data sources, and what's next.
Feature engineering is a growingly critical requirements in the MLOps lifecycle - Doordash engineering presents their internal system "Fabricator". In this article they provide an architectural and conceptual overview of their system, together with their requirements, data & control planes, features, examples, limitations, and future developments.
The AI O'Reilly team has put together a great introduction into Causal Inference, where they provide a high level overview, together with useful resources, as well as insights across a broad range of sub-fields.
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
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