Issue #294 - The ML Engineer ??
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, ACM, LF, etc | Join 60k+ ML Newsletter
Thank you for being part of over 60,000+ ML professionals and enthusiasts who receive weekly articles & tutorials on Machine Learning & MLOps ?? You can join the newsletter for free at https://ethical.institute/mle.html ?
If you like the content please support the newsletter by sharing with your friends via ?? Email, ?? Twitter , ?? Linkedin and ?? Facebook !
If you are a Machine Learning Practitioner looking for an interesting opportunity, I'm currently hiring for an Applied Science Manager for Forecasting & Causal Inference for a Senior Applied Scientist for Forecasting - do check it out and do feel free to share broadly!
This week in Machine Learning:
Chip Huyen shares great insights and best practices on production-grade generative AI platforms: Great article detailing key best practices for production GenAI, including context enhancement, guardrails, model routers, gateways, and caching to optimize performance and security. This is quite an emerging field so it's interesting to see latest paradigms to address known challenges, including observability through metrics, logs, and traces, and the use of AI pipeline orchestration to manage complex workflows.
Amazon migrated their exabyte-scale BI data processing platfrom from Apache Spark to Ray on Amazon EC2, and they share key learnings through this journey. This switch was driven by some of the limitations they were facing with Spark when handling larger datasets requiring nuanced cost efficiency and faster processing times. Despite initial challenges across job success rates and suboptimal memory utilization, Ray has proven to be significantly more cost-effective, translating to substantial annual savings.
Google's Tensor Processing Units (TPUs) were developed over a decade ago to address the increasing AI compute demands - today Google shared their journey across the last 10 years: Throughout the last decade, TPUs have evolved significantly enhancing performance and efficiency across ?large scale compute. These AI-specialized chips now support advanced models like Gemini 1.5 Flash and are integral to many Google (+ of course DeepMind's) products and services.
A fantastic visual guide to quantization, explaining the most popular technique to reduce the size of Large Language/Foundation Models: This growing technique of quantisation lowers the bit-width of numerical representations, and leverages techniques such as post-training quantization (PTQ) and quantization-aware training (QAT) which optimize models to use lower precision without significant accuracy loss. There are also other methods like GPTQ, GGUF, and BitNet which allow for extreme reductions to 4-bit and even 1.58-bit representations, making it feasible to run large models on consumer hardware with limited VRAM whilst still maintaining reasonable performance.?
A great set of lessons learned from 35 years of software development: 1) Prioritize simplicity in solutions and frequent releases to create value swiftly; 2) build strong relationships within and outside your company to advance and realize your vision; ensure visibility of your work; 3) embrace new challenges to grow skills; 4) pursue passion over titles; 5) and remember that software is transient, so focus on delivering functional increments rather than perfect solutions.
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.?
Upcoming conferences where we're speaking:
Other upcoming MLOps conferences in 2024: ?
In case you missed our talks:
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!
OSS: Policy & Guidelines
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 European research centre that carries out world-class research into responsible machine learning.
? Infrastructure Engineer ? DevOps ? SRE ? MLOps ? AIOps ? Helping companies scale their platforms to an enterprise grade level
3 个月This week’s developments in the Machine Learning Ecosystem offer invaluable learning opportunities for ML Engineering enthusiasts. What aspects are you most interested in exploring further?