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The MachineLearning EngineerIssue #357

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#356
Issue #35719/10/25ai-agentsreinforcement-learning
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Issue #357 🤖 - Claude Skills Driving Agentic Innovation, Truths in Software that are False in AI, Karpathy on the Decade of Agents, Blameless Culture in Engineering + 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:

Claude Skills Driving Agentic Innovation

Anthropic’s new Claude Skills system released last week may be more transformative than the MCP craze due to a few advantages: Claude Skills are opening more possibilities for lightweight, modular extensions to LLMs using simple Markdown files and optional scripts that Claude can load directly. These “skills” let models perform specialized tasks efficiently within a coding environment, which significantly reduces token overhead and complexity compared to MCP’s heavy protocol structure. Although it can be argued that these two frameworks can be compatible / interchangeable in the future, it is interesting to see this wild-wild west of competing approaches to find the global optimal. This is a great write-up by Simon Willison which summarises both the advantages as well as some practical examples.

Truths in Software that are False in AI

Here are 5 truths in regular software that are false when applied to AI: 1) Software vulnerabilities are caused by mistakes in the code; 2) Bugs in the code can be found by carefully analysing the code; 3) Once a bug is fixed, it won’t come back again; 4) Every time you run the code, the same thing happens; 5) If you give specifications beforehand, you can get software that meets those specifications. It is becoming growingly accepted that assumptions from traditional software engineering like bugs, testing and monitoring, don’t apply in the same way to modern AI systems. Unlike conventional software, in AI behavior is learned from often large datasets rather than explicit instructions, making errors unpredictable, non-deterministic, and resistant to debugging. There is now a growing consensus across organisations to accept these differences in order to derive the right practices to ensure stable and robust operations at scale.

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 🔥

Karpathy on the Decade of Agents

Some really insightful take-aways on this 2 hour podcast with Andrej Karpathy, such as arguing that “we are in the decade of agents, not the year of agents” - here are 5 key takeaways: 1) It will take over a decade to properly figure out agents, as we’re in the early stages of memory, multimodality and reliable long-horizon reasoning. 2) Reinforcement learning is inefficient and noisy, requiring new approaches like process-based supervision and reflection. 3) Large language models suffer from model collapse and over-memorization, limiting creativity and continual learning. 4) Coding is currently the most practical application for LLMs, but they struggle with truly novel or complex system design. 5) AGI’s arrival will be gradual with steady productivity growth rather than causing a sudden economic or societal leap. For machine learning practitioners, understanding where the real bottlenecks lie determines whether we build the foundation required for robust agentic systems, or get stuck on the hype.

Blameless Culture in Engineering

There are critical lessons that we can take on from the movement that drives the “Blameless culture in engineering”, which focuses on solution vs finger-pointing: Highlights that high-performing engineering and ML operations teams thrive on a blameless culture, where failures are treated as opportunities to strengthen systems rather than assign fault. This by no means encourages removing accountability, but switches the focus from finger-pointing towards short- and long-term solution finding instead; this ensures problems are resolved efficiently, which then can allow space for systemic or organisational improvements required. In production machine learning and large-scale engineering, culture is as critical as the code itself, and there are key lessons that can be adopted from this because even the best systems fail without trust, learning, and accountability.

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!

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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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