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Issue #175
This #175 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 #175:
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!
Join us for this hands on online session where we'll dive into the top security vulnerabilities present at every stage of the ML lifecycle throught practical "Flawed ML Security" examples, as well as cover hands on MLSecOps best practices to address them at scale.
An interesting article from food delivery service Wolt showcasing their journey developing and scaling MLOps across their organisation, including concepts, lessons learned, challenges, and interesting insights throughout the various lifecycle phases of production ML.
A mind blowing overview of applications of OpenAI's code generation model. This post provides some examples of how this model has been used to generate games from text counterparts, which are quite astonishing as they can also be tested interactively thorugh the codepen examples.
The developer survey from GitLab has been released with very interesting insights on the Devops ecosystem. This year's edition provides particularly interesting insights on the rise of AI/ML in devops as well as vice-versa, including relevant trends and metrics.
As we celebrate the 50th anniversary of the C programming language, this post covers very insightful perspectives on how C has evolved from a programming language into a cross-industry standardised protocol adopted globally, covering resources, examples and concepts that outline its shortcomings and impact.
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