Issue #203 - THE ML ENGINEER ??
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, EU, ACM, etc | Join 60k+ ML Newsletter
This 203 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 10,000+?subscribers. 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 the MLE 203 :?
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!
Operating machine learning systems at scale introduces complex challenges that go beyond traditional software systems. In this opening keynote at the KubeCon Kubernetes AI day I covered key trends that have been developing in the MLOps ecosystem, diving not only into technological trends, but also growing trends around organisational / team structures and processes.
If you missed the KubeCon conference, you can still catch up with my talk on Metadata for End-to-End MLOps systems by joining our upcoming webinar this Thursday! In this session I provide an intuition for the need of robust metadata; I cover how production MLOps systems introduce different challenges to the traditional data management space, as well as robust solutions available today.
The O'Reilly team explores growing trends in November 2022 across the technological landscape. This resource dives into a broad range of areas, including AI, General Programming, Security, Quantum and more.
Former Tesla AI Director Andrej Karpathy has released a free online course covering Neural Networks "Zero to Hero". In this course he dives into the foundational theory and practical examples to develop a robust understanding on the core concepts that revolve around Neural Networks.
Reviewing mathematical foundations is key for both computer science and machine learning. This great resource from the University of Pensilvania contains a comprehensive compendium of algebra, topology, differential calculus and optimization theory, which provide fantastic content for technical practitioners that want to develop and polish their foundational knowledge.
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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:
Other relevant upcoming MLOps conferences:
?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 outlined below.
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!
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.