Issue #220 - THE ML ENGINEER ??
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, ACM, LF, etc | Join 60k+ ML Newsletter
This 220 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 25,000+?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 ML Engineer:
Thank you for being part of over 25,000 ML professionals and enthusiasts who receive weekly articles & tutorials on production ML & MLOps ?? If you havent, you can join for free at https://ethical.institute/mle.html ?
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just 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!
Productionising Machine Learning Systems at Scale is one of the biggest challenges this year, and Large Language/Image models introduce complex challenges ?? Our talk from PyData Global 2022 is now on YouTube, and provides a detailed overview of the challenges and solutions for productionising Large Image/Text/Anything Models. In this resource we take a relatively amusing approach, where we deploy a ML Pipeline with a GPT model as the pre-processor and a text-to-image GenAI model as the post-processor. This allowed for a "creative" workflow where images are created from a single word, into a generated phrase, into an image. The code is fully open source so do test it out or please do contribute with a PR ??
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective ?? Cambridge researchers share an insigthful and comprehensive survey on production machine learning systems demystifying the emerging topic of data-centric ML. This research paper focuses particularly on challenges and insights around deployment, monitoring and maintenance of machine learning systems.
Real time high-performance data at Coinbase ?? An interesting series from Coinbase discussing their journey tackling high-performance data challenges across their organisations at scale. In this resource they provide useful information on their architecture, technologies, benchmarks, principles and next steps.
NVIDIA OSS Recommender System meets the MLOps ecosystem: building a production-ready RecSys pipeline on cloud ??This post provides a high level overview of production challenges when adopting and productionising recommender systems. It provides a practical example to tackle a real-life challenge, providing an intuition on the architecture as well as code for training, testing, serving and beyond using ecosystem tooling such as Metaflow, DBT, and more.
20 Lessons from 20 years in developing software ??? A great high level article providing 20 points of advise from a long career in software engineer, aiming to outline a set of principles that are important to consider for a meaningful and consciencious approach to software as a applicable and useful craft.
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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 spoke at recently with published video:
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.