Issue #174 - THE ML ENGINEER ??
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, ACM, LF, etc | Join 60k+ ML Newsletter
This #174 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 #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 [email protected] ! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
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.
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.
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.
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.
领英推荐
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.
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:
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 !?
?OSS: Awesome AI Guidelines
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.