Issue #286 - The ML Engineer 🤖
AI in Engineering at Google, The LLM Fine Tuning Index, Bayesian Analysis Book, Alibaba's LLM, Microsoft's Foundation Model + more 🚀
Tomorrow I'll be in San Francisco speaking at the Data & AI Summit with other incredible speakers such as NVIDIA's Jensen Huang, Stanford's Fei-Fei Li, Databricks' Ali Ghodsi + many more! I'll be giving a talk on "Building a multi-petabyte data platform at Zalando", if you're around come say hello 👋!
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This week in Machine Learning:
AI in Engineering at Google
The LLM Fine Tuning Index
Free Bayesian Data Analysis Book
Alibaba's Latest LLM Qwen2
Microsoft's Climate Foundation Model
Upcoming MLOps Events
Open Source ML Frameworks
Awesome AI Guidelines to check out this week
+ more 🚀
Come say hello at San Fran Data & AI Summit 👋 !!
Google just published exciting results on their internal adoption of AI/ML for developer productivity, surprisingly reporting that 50% of their code (!!!) is now being written by AI: Google's analysis shows a clear double down towards investing in AI for aid software engineering, as they have reported improvements across both dev productivity and dev satisfaction. Some of the key drivers from this success seem to be attributed to high-quality data, iterative learning, and intuitive UX integration. Google also shows strong ambitions to expanding AI adoption into testing, code understanding, and software maintenance. This is both promising and interesting, certainly a promising space to keep an eye on.
Finetuning LLMs is becoming growingly commoditised - this LLM Fine Tuning Index provides a great benchmark to even surpass GPT-4 performance with OSS models, and understand the "what", "when" and "how much $$" of LLM finetuning, with practical results from highly popular OSS models such as Llama, Zephyr, and Mistral across over 700 fine-tuning experiments.
Free Bayesian Data Analysis Book
One of the best books on Bayesian Data Analysis is available for free, covering key fundamentals like probability and inference, single and multiparameter models, and hierarchical models - great opportunity for practitioners to expand key fundamentals and advaced concepts. This is a great resource to go from basics to the more advanced nuances, such as computational techniques like Markov chain Monte Carlo and Hamiltonian Monte Carlo, as well as practical deep dives across other topics like regression models and nonparametric methods.
Alibaba's latest foundation model release has been taking the world by storm with a 0.5B parameter model with an impressive 32k context length (128k tokens for larger model). This new release comes with five models ranging from 0.5B to 72B parameters, support for 27 languages, and improved performance in coding, mathematics, and long-context tasks.
Microsoft's Climate Foundation Model
Microsoft enters the Forecasting Foundation Model Race with their latest release of Aurora, a 1.3 billion parameter model tackling atmospheric forecasting trained on over a million hours of diverse weather and climate data. This foundation model produces five-day global air pollution predictions and ten-day high-resolution weather forecasts, claiming higher performance compared to specialized models. Similar to previous foundation models from Amazon, Google, Nixtla, etc it will continue to be an important effort to verify performance against open benchmarks to continue to see innovation and improvement.
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
MLOps World - 8th November @ USA
In case you missed our talks:
Responsible AI Workshop Keynote - NeurIPS 2021
Practical Guide to ML Explainability - PyCon London
ML Monitoring: Outliers, Drift, XAI - PyCon Keynote
Metadata for E2E MLOps - Kubecon NA 2022
ML Performance Evaluation at Scale - KubeCon Eur 2021
Industry Strength LLMs - PyData Global 2022
ML Security Workshop Keynote - NeurIPS 2022
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.