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The MachineLearning EngineerIssue #286

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Issue #286 🤖 - 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 + 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:

Come say hello at San Fran Data & AI Summit 👋!!

AI in Engineering at Google

Google just published promising results integrating AI/ML towards internal developer productivity, reporting 50% of their code being written by AI (which is indeed surprising): Google’s analysis shows a clear double down towards AI services that aid software engineering, showcasing improvements across both dev productivity and dev satisfaction. Some of the key improvements stem from from high-quality data, iterative learning, and intuitive UX integration. They also highlight some strong ambitions expanding AI applications to testing, code understanding, and maintenance. This is both promising and interesting, certainly an interesting space to keep an eye on.

The LLM Fine Tuning Index

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: This LLM Fine Tuning Index from Predibase includes practical results from 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. 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 LLM Qwen2

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, tackling atmospheric forecasting trained on over a million hours of diverse weather and climate data. This 1.3 billion parameter 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:

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.

  • 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.

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