Issue #195 - THE ML ENGINEER ??
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, ACM, LF, etc | Join 60k+ ML Newsletter
This #195 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 #195:?
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!
Defining standardised best practices analogous to team / code standards is key for scalable Machine Learning projects and systems. This document presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming.
The National Cyber Security Center has released a fantastic resource on Machine Learning Security that provides a framework to ensure best practices at every stage of the model lifecycle. This resource covers quite a comprehensive set of applicable best practices.
The challenge of extracting text from images has seen evolving tools, progressively introducing more impressive capabilities. It is great to see the open source solutions taking the lead by leveraging state of the art machine learning models. This tutorial showcases how to use PaddleOCR with pretrained and custom models for text extraction and visualisation of results.
Applications of machine learning in graph-like structures continues to become a growingly popular due to the applicability to real world challenges. The team at Alibaba presented an interesting approach to leveraging these relationships and provided insights into a practical usecase introducing it to large scale online and offline recommendations based on click-through-rate.
PapersWithCode is a fantastic initiative that advocates for reproducible research, relating a large repository of research papers that are accompanied by reproducible code. They also provide insigthful temporal analytics and insights that showcase popularity metrics for frameworks and code.
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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.