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Issue #174
THE ML ENGINEER 🤖
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This week in Issue #174:
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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A very comprehensive and intuitive explanation of how spotify uses semantic search for podcasts, providing visual and conceptual overviews, as well as hands on code to showcase some of the interesting and practical aspects.
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The team at Microsoft has put together an interesting research paper that explores the challenges for data drift in production ML systems at scale. They propose a new solution that suggests to tackle the challenges with flexibility and scale by design.
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The ZenML team has put together a fantastic overview deploying machine learning models end to end through their new integration with Seldon Core. They showcase the hands on steps to deploy a FashionMNIST model into an EKS kubernetes cluster through a programmatic Kubeflow pipeline.
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A really insightful article by various thought leaders in the container orchestration space, where they discuss some lessons learned from three widely known container scheduling systems including Borg, Omega and the now widely adopted and fast growing Kubernetes.
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The Data Exchange podcast dives into conversation with Jack Clark on their recent published annual AI Index report. In this episode they cover recent progress in Deep Learning, the raise of AI ethics, and what lies ahead of language models.
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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:
- 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.
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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
