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The MachineLearning EngineerIssue #358

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Issue #35826/10/25forecastingllmsmlops
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Issue #358 🤖 - Amazon Releases Chronos-2, Robotics Foundation Models, DeepSeek OCR Released, Ray Joining Linux Foundation + more 🚀

The survey to help us map out this year’s State of Production MLOps in 2025 is still OPEN FOR RESPONSES 🚀🚀🚀

If you have a few minutes, your contribution will make a significant difference to the whole production ML ecosystem 🥳 The results will be shared as open source like last year!! You can add your response directly at:

bit.ly/state-of-ml-2025 🔥

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This week in ML Engineering:

Amazon Releases Chronos-2

Really exciting to see Amazon’s latest move in the time-series foundation model (aka GenAI) race releasing Chronos 2.0; believe it or not, time-series foundation models have been showing impressive practical applications in industry, and this new model seems brings some exciting features that addresses key limitations from its predecessor: Chronos-2 is Amazon’s universal time series foundation model that generalizes beyond univariate forecasting to handle multivariate and covariate-informed tasks in a zero-shot manner through in-context learning. This new architecture introduces group attention to model dependencies among co-evolving series and integrates both historical and known-future covariates without additional training. The benchmarks have also evolved since Chronos v1, which enable for more robust evaluations, and it seems there are strong accuracy improvements over prior models. This is quite an exciting field of research and application that is already having real-world impact in industry - I personally am very much looking forward to seeing the evolution in the next year!

Robotics Foundation Models

Everyone is talking only about LLMs is missing the exciting other AI revolutions taking place right now; robotics foundation models are one of the more exciting areas of research no-one is talking about: Dexterous robotic control has been transforming industries, and the ability to universally transfer learn can have huge opportunity in factories, fulfillment centers and basically any dynamic real-world environments. Some recent SotA models are built on Vision-Language-Action (VLA) systems and showing better performance than domain-specific models. It seems it’s now possible to take general models across e.g. robotic arms, and support co-learning together with human guidance to accelerate the capabilities to carry out tasks that have strong generalization and robustness on long-horizon manipulation. This is yet another interesting field of research that is seeing practical application, and is transforming industries with impact that we’ll see slowly and then all at once.

The State of MLOps 2025 Survey 🔥

We are still continuing to gather the insights on this year’s MLOps Survey! We still need your support to continue collecting diverse perspectives to map the ecosystem! Please help us with your response, as well as by sharing with your colleagues 🚀🚀🚀 If you have a few minutes, your contribution will make a significant difference to the whole production ML ecosystem 🥳 The results will be shared as open source like last year!! You can add your response directly at: bit.ly/state-of-ml-2025 🔥

DeepSeek OCR Released

DeepSeek-OCR shows some exciting breakthroughs for LLMs; we may be able to extract more signal and meaning from text through their pixel-related images than their UTF-8 machine representation: DeepSeek-OCR demonstrates that document images can serve as a highly efficient context representation for language models, achieving 7–20× token compression while preserving up to ~97% OCR decoding accuracy at moderate ratios by replacing text tokenization with a unified visual input stream processed by the proposed DeepEncoder architecture. Andrej Karpathy has put it quite succintly following this DeepSeek release; if pixels become the universal interface, we can break free from tokenizer limits, which could unlock radically more efficient context handling and treat real-world documents as first-class citizens in language models. This approach eliminates tokenizer fragility, enables bidirectional attention over inputs, and naturally supports multimodal elements such as layout, charts, math, and chemical structures. Sometimes breakthroughs in innovation happens in areas that you may least expect, and retrospectively these may feel as obvious.

Ray Joining Linux Foundation

Ray is joining the Linux Foundation; I have to say this is one that I am extremely excited for, particularly having been involved since the early days of the LF AI & Data Foundation, as well as the early days of Ray, this is a perfect match. Distributed AI infrastructure has today become the decisive factor in whether organizations can deploy and scale state-of-the-art models in production, and organisations have still not standardised into a single framework. Ray has been growing as a potential contender showing practical applications at scale, and them joining the PyTorch Foundation under the Linux Foundation really has the potential to solidify its position as the open-source distributed compute engine for large-scale AI. This is a very exciting space, and especially with the current state of heterogeneity in the ecosystem, there are significant opportunities for consolidation across the ecosystem.

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 2025:

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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