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Issue #189
THE ML ENGINEER 🤖
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If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
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This week in the MLE #189:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
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Identifying best practices and tools for Machine Learning Security is key, which is why we are thrilled to release "The MLSecOps Top 10". This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle. This project aims to provide an evaluation of security vulnerabilities analogous to the "OWASP Top 10 Report" but with a focus on machine learning security. The resources are open source and include examples, tools, best practices and next steps, including our contributions to the Linux Foundation Trusted AI
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At Uber’s scale, thousands of microservices serve millions of rides and deliveries a day, generating more than a hundred petabytes of raw data. This article provides a great insight on how Uber built their centralized workflow management system based on Airflow and re-architected to achieve massive scale. This includes core principles, previous systems, considerations, challenges and next steps.
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Research papers can provide machine learning practitioners with fantastic state-of-the-art capabilities which is why developing the skill to implement papers into code can be key. This fantastic resource has put together a set of well documented and intuitively explained implementations of research papers into PyTorch code, providing line-by-line explanations of the approach.
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DeepMind shares an exciting milestone releasing an open resource containing a vast amount protein structures predicted from the AlphaFold project. This is quite an exciting milestone as it promises significant advancement with the vast applicability of this resource, and it has already been seen to enable for outstanding case studies.
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Building a robust understanding on statistical foundations can be key for machine learning practitioners. This resource provides freely available videos and lecture notes from the "Summer school on Statistical Physics & Machine learning" that took place this month, containing a vast range of key content on statistical foundations.
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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:
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EuroSciPy - August 29th @ Basel [Industry-strength DALL-E]
Other relevant upcoming MLOps conferences:
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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!
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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:
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© 2018 The Institute for Ethical AI & Machine Learning
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The Institute for Ethical AI & Machine Learning
