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The MachineLearning EngineerIssue #318

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Issue #318 🤖 - AI Engineer 2025 Reading List, Causal Inference meets Deep Learning, Jensen Huang NVIDIA Keynote, OpenAI’s Economic Blueprint, Papers Every Dev Must Read + more 🚀

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This week in Machine Learning:

If you’re looking for an interesting career opportunity, I’m hiring for a few roles including Data Science Manager (Forecasting), as well as Principal Product Manager (Forecasting) - check them out and please do share with your network!

AI Engineer 2025 Reading List

What better way to start 2025 than with an AI Engineering curated list of all-the-best research papers! This is a great resource bringing together around 50 resources on AI Engineering that span across frontier LLMs, benchmarks/evals, prompting, retrieval-augmented generation, agents, code generation, vision, voice, image/video diffusion, and more. This is quite a comprehensive resource which should keep us busy for a while - if there’s any papers missing do make sure to contribute them upstream!

Causal Inference meets Deep Learning

The intersection between Causal Inference and Deep Learning is A fascinating area of research - this research paper provides a really comprehensive overview on the current state of the ecosystem: This paper provides a great introduction to the foundational concepts in Causal Inference, and then links to literature that explores deep learning applications (e.g., adversarial methods, contrastive learning, reinforcement learning, and diffusion models). This intersection of research has quite a breadth of applications - in industry there’s quite a broad use-case across natural language processing, marketing intelligence, graph representation, and computer vision. There’s also clearly several really interesting challenges that remain to be solved, such as limited interventional data, difficulties in confounder identification, and the need for benchmarks. This is certainly a space to keep a close eye as it develops!

Jensen Huang NVIDIA Keynote

NVIDIA CEO Jensen Huang delivered quite an insightful talk at the GTC 2025, which provided a great overview of the past and the future: This keynote provided a surprisingly great retrospective into the history and breakthroughs of GPUs in graphical applications (first), and then AI workloads. It is interesting to see how ubiquitous the the rise of agentic AI has become, and the push for this field to be the next gold-mine. And of course there was a lot of hype towards robots in general as well, as nvidia delves further into the hardware / robotics space.

OpenAI’s Economic Blueprint

OpenAI proposed an Economic Blueprint as a gold-standard for AI at a national level - it is interesting to see organisations pushing national agendas at this stale: OpenAI pitches for a centralized, federal-led approach to AI regulation and infrastructure, which in USA-terms would replace state-by-state rules on AI, which is proposed to spur innovation and reinforce national security. It is interesting to see that the areas proposed are quite intuitive, such ascoordinated investments in chips, energy, data, and workforce development, alongside clearer “rules of the road” for safe, responsible AI deployments - however with a clear bias towards the technologies that OpenAI depends on.

Papers Every Dev Must Read

Yet another article on “Papers Every Developer Should Read”, however we can’t get enough of these for 2025: This is another great article which brings classic computer science papers together. As always these cover key areas across system design, distributed computing, data storage, metrics, infrastructure, and performance - and more. If you haven’t had a chance to catch up on some of these, now is your chance!

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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