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Issue #162
 This #162 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 #162:
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!
The MLOps London meetup comes back this week with a fully online edition bringing together insights from FuzzyLabs Cofounder Matt Squire and Trainline Principal Data Scientist Jan Teichmann to share knowledge on various practical production MLOps topics.
An interesting practical tutorial series that provides hands on insights on production architectures for MLOps, specifically focusing on the feature extraction, process management, serving infrastructure and monitoring phases of the ML lifecycle with a practical use-case.
A hands on article convering an interesting perspective into the software engineering best practices of unit testing applied into data science, covering a set of practical examples.
The H2O team has put together a comprehensive overview into one of the more popular methods in the machine learning explainability space, namely shapley values, together with a set of examples into how these values can be calculated for a feature.
A recently released documentary which covers the history of Kubernetes and container applications - this is the first part of an upcoming series of episodes.
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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