Issue #171 - THE ML ENGINEER ??
Alejandro Saucedo
AI & Data Executive @ Zalando | Advisor @ UN, EU, ACM, etc | Join 70k+ ML Newsletter
This #171 edition of the ML Engineer newsletter contains curated articles, tutorials and blog posts from experienced Machine Learning and MLOps professionals
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This week in Issue #171:?
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A fantastic resource providing an extensive list of freely available educational courses and videos in youtube related to various applied and theorietical topics in the machine learning space.
This article provides great insights on key trends in the data management space, including growing trends on open source, cloud, SaaS, serverless, and more.
An extensive and comprehensive overview of different similarity metrics and texte embedding techniques with both practical and intuitive examples
OpenTelemetry has been growing with the potential to become the future of instrumentation, which is growingly important in the MLOps space to ensure robust observability infrastructure and techniques
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An insightful article sharing lessons learned and insights from building, extending and maintaining a successful and popular open sourcee project through various phases of the project lifecycle.
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:
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!
About us ? The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning.