Issue #197 - THE ML ENGINEER ??
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, ACM, LF, etc | Join 60k+ ML Newsletter
This #197 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 #197:?
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!
Real time machine learning continues to be adopted by organisations at growing speed. This article by the Instacart engineering team covers an insightful deep dive into how they have been able to move from batch inference into real time processing & serving of machine learning data.
Berkeley researchers have released an interesting study exploring the ecosystem operationalising machine learning. This resource shares findings from 18 ML engineer interviews across a wide variety of sectors, and shares challenges, trends and more.
One of the core trends of data processing frameworks are adoption of Kubernetes as their backend orchestrating engine. This article provides practical examples that can be adopted to "automate the boring stuff" around administration of clusters.
Building an intuition around how data storage & processing work under the hood can help enrich the technical capabilities of ML practitioners. This resource attempts to share insights on database-internals by building a simple NoSQL database from scratch using the Golang programming language.
Energy consumption has become growingly important due to growing climate concerns. This article asks the interesting question of what programming languages require most / least energy to carry out a simple set of (unoptimised) instructions. Spoiler: unsurprisingly low-level > high level languages.
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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.