Issue #191 - THE ML ENGINEER ??
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, ACM, LF, etc | Join 60k+ ML Newsletter
This #191 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 #191:?
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!
Keeping with growing trends in key emerging technologies is key. The O'Reilly team has published the "Radar Trends" which compiles key trends to watch for the month of August. This dives into a broad range of areas including AI, Security, Programming, Web, Quantum and more.
The lifecycle of a machine learning model begins once it's deployed to production. This article provides a great practical tutorial introducing advanced data science monitoring techniques to have real-time observability through outlier detectors, drift detectors, etc.
The vast amount of options and tools available for each stage throughout the machine learning lifecycle make the ability to select relevant technologies a growing challenge. The ZenML team has put together a platform that brings together the best tools at each phase of the ML lifecycle. In this podcast ZenML cofounders Hamza & Adam dive into the topic of end to end MLOps, including trends, challenges and opportunities.
Developing end-to-end recommender systems introduce complex infrastructure demands from an MLOps perspective. This article provides an interesting practical example developing a two-layered recommender systems, introducing the key requirements, architecture, data sources, inference, scheduler, feature store, and several other deeper dives.
The SciPy 2022 conference videos are out now avilable for free. This year's event brought togeter insightful sessions providing useful practical and conceptual deep dives.
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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.