Issue #276 - The ML Engineer 🤖

Scaling ML Infrastructure at Uber, Largest OSS LLM from Databricks, Google AI Forecasting Floods, Big Data XKCD, AI Accountability + more 🚀

Mar 31, 2024

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This week in Machine Learning:

Scaling ML Infrastructure at Uber
Largest OSS LLM from Databricks
Google AI Forecasting Floods
Big Data Skills through XKCD
AI Accountability Report from NTIA
Upcoming MLOps Events
Open Source ML Frameworks
Awesome AI Guidelines to check out this week
+ more 🚀

Scaling ML Infrastructure at Uber

Uber's journey scaling their AI/ML infrastructure: Uber's AI infra has evolved significantly in the past few years, transitioning to cloud infrastructure, enhancing CPU and GPU hardware, and refining their Michelangelo platform to meet growing model complexities. Uber measures AI infra efficiency through utilization, reliability and developer velocity, which they have managed through optimizing existing infra through unified workload scheduling, network and memory upgrades for training efficiency, and cost-effectiveness approaches to their cloud approach. A great compendium on large scale optimisations for machine learning systems and machine learning operations at a leading tech giant.

Largest OSS LLM from Databricks

Databricks has released the largest and highest-performant LLM to date: Databricks releases their new language model under the codename "DBRX", with benchmarks that suggest better performance compared to GPT-3.5, and on par with Gemini 1.0 Pro. As many other successful architectures, this mode consists of a mixture-of-experts architecture, showcasing performance that suggests 2x improvements. The best feature is that it is an open release, raising the bar for the open-AI-race which is seeing developments on a weekly basis!

Google AI Forecasting Floods

Google Research releases their AI-forecasting approach for global flood forecasting: A great high level piece from google showcasting accurate prediction of riverine flooding up to seven days in advance across over 80 countries, including those with scarce data and vulnerable regions. This piece of research tackles advancement in the historical challenge of flood forecasting at scale, primarily due to the complex nature of the problem and the limited availability of streamflow gauges worldwide leveraging standard LSTM deep learning models.

Big Data Skills through XKCD

Learning big data skills with the internet's favourite XKCD comics: A great resource to learn best practices and of big data processing by identifying the most cited XKCD comics on Hacker News. In this resource we explore the nuances analyzing comments and stories in Hacker News using BigQuery, standard SQL and basic scripting. As part of this exercise we follow standard steps such as extract comic IDs, deduplicate entries, and resolve different URL formats to standardize comic references - resulting in the top ten most cited XKCD comics on Hacker News.

AI Accountability Report from NTIA

The National Telecommunications and Information Administration releases the AI Accountability Policy Report: The NTIA releases a great report on AI accountability, providin ga set of recommendation for robust accountability mechanisms in the development and deployment of AI systems. The recommendations encompass: 1) guidance for AI audits, 2) enhanced information disclosures, 3) clarification of liability standards, and 4) regulatory requirements for high-risk AI systems.

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:

AI in Production 2024 @ 15th February @ Virtual
WeAreDevelopers 2024 @ 17th July @ Germany
Data Intelligence Day 2024 @ 9th April @ Germany

Other upcoming MLOps conferences in 2024:

FOSDEM (AI & HPC) - 3rd February @ Belgium
State of the Open (Data) - 6th February @ UK
World AI Cannes - 8th February @ France
NVIDIA GTC - 17th March @ USA
KubeCon Europe (AI) - 18th March @ France
MLConf - 28th March @ USA
PyData & PyCon DE - 17th April @ Germany
MLSys - 13th May @ USA
ODSC West - 29th October @ USA
Data & AI Summit - 10th June @ USA
AI Summit London - 12th June @ UK
World Summit AI - 9th October @ Neatherlands

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.

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!

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:

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!

About us

The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.

The Machine Learning Engineer