Building A Generative AI Platform 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’s Exa-Migration to Ray 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. |
|
|---|
|
10 Years of TPUs at Google 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 Visual Guide to Quantisation 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. |
|
|
|---|
|
Lessons of 35 Years in Software 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:
|
|
|---|
| | |
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. - Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
- CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
- Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
- CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
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: - MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
- AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems.
- An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles.
- ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018.
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!
|
|
|---|
| | |
| | | | The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning. | | | | |
|
|
|---|
|
|
This email was sent to You received this email because you are registered with The Institute for Ethical AI & Machine Learning's newsletter "The Machine Learning Engineer"
|
| | | | |
|
|
|---|
|
© 2023 The Institute for Ethical AI & Machine Learning |
|
|---|
|
|
|
The Institute for Ethical AI & Machine Learning
