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The MachineLearning EngineerIssue #335

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#334
Issue #33518/05/25ai-agentsgenerative-aimlops
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Issue #335 🤖 - OWASP Agent Name Service, OpenAI's New Software Engineer, On Complex Systems at Scale, Google on Text-to-SQL models, DeepMind on Coding Agents + 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 Data Scientist (Forecasting) - check them out and please do share with your network!

OWASP Agent Name Service

The OWASP Agentic Team has released a new framework for discovery and verification of agents: The Agent Naming System (ANS) provides DNS-inspired universal registry for AI agents, PKI-based identity verification (X.509), Capability-aware discovery, Protocol-agnostic design (A2A, MCP, ACP, etc), Threat Analysis and more. It was great to contribute as part of the reviewing board of the OWASP Agentic team - check it out!

OpenAI’s New Software Engineer

OpenAI releases a new software engineering agent that runs on repo sandboxes to tackle multiple coding tasks in parallel - it’s interesting to see OpenAI playing catchup (and catching up!) on domain-specific agent services: This new coding agent claims to cover the ability to fix bugs and write new features (via direct pull requests) as an army of supporting junior developers in your toolbox. It seems production teams can customize Codex’s behavior with (yet another config) AGENTS.md files to mirror their development conventions and ensure environment fidelity, and can also use the lightweight Codex CLI locally with the optimized codex-mini model for low-latency edits and Q&A.

On Complex Systems at Scale

As production software practitioners we often have to deal with both, “complicated” and “complex” systems - it is important for us to make a clear distinction to ensure the right solution: Complex systems exhibit unpredictable, emergent behaviors, delayed and nonlinear effects, hysteresis, and counterintuitive local vs. global trade-offs. Practitioners building production ML platforms should therefore favor reversible changes (feature flags, canaries, progressive rollouts), instrument high-cardinality observability, validate via simulations and workload replays, define both local and end-to-end metrics to catch hidden impacts, and lean on adaptive ML models and strong cross-functional collaboration to navigate ambiguity and continuously evolve their systems.

Google on Text-to-SQL models

Google is tackling the challenge of AI-powered text-to-SQL models through Gemini, and they share some of the key challenges when generating SQl with ML models: The Google team has been able to collect insights on the most common limitations on LLMs that generate SQL from across their systems (BigQuery, CloudSQL, Spanner, etc); they find some relevant areas include injecting business-specific context via semantic retrieval (vector-search on schemas, data samples, and annotations), managing user-intent ambiguity through LLM-driven clarification dialogs, and ensuring dialect fidelity with self-consistency voting, dry-run validation, and targeted reprompting or lightweight fine-tuning. This area of automated SQL generation with LLMs is only becoming more common in the analytics and engineering space so it is definitely great to see best practices shared from lessons learned.

DeepMind on Coding Agents

DeepMind releases AlphaEvolve as a new coding agent to also challenge the competition (ie Github, Claude, now openAI, etc): As other models it enables generation, evaluation and refinement with Gemini Flash - so far it seems that most players are consolidating on features so it will be interesting to see how competition pushes towards new features beyond improved performance.

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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