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THE ML ENGINEER 🤖
Issue #153
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This week in Issue #153:
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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The O'Reilly team has released an overview of key highlights identified in the areas of artificial intelligence, ethcis in technology, general programming, security, infrastructure, IOT, web and quantum computing.
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EIML Institute Contributor & Thoughtworks Principal Data Engineer Ryan Dawson has published an analysis of MLOps platforms across various different categories and capabilities.
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A great resource bringing together a urated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code.
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The Data Exchange podcast dives into conversation with Virginia College Researcher Nikhil Muralidhar into his paper "Using AntiPatterns to avoid MLOps Mistakes" and covers insights from learnings obtained in the financial services industry.
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A fantastic refresher for MLOps practitioners from booking.com SRE Ivan Velichko on the core concepts & intuition of container fundamentals.
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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.
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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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