 | State of Prod ML 2024 Survey Dire results on Diversity from the 2024 Survey on the State of Production Machine Learning; only under 5% of respondents identifying as female - there is a lot of work for all of us in the Prod ML ecosystem! These have been really important insights from 2024 Survey on The State of Production ML; we have designed the questions to provide meaningful insights on the current landscape of production ML in 2024 - if you have a chance we would be grateful if you could spend a few minutes on the survey, as you'll contribute valuable information about the machine learning tools and platforms you use in your production ML development. Your input will help create a comprehensive overview of common practices, tooling preferences, and challenges faced when deploying models to production, ultimately benefiting the entire ML community 🚀 We are also working on an interactive visualisation for everyone to be able to slice and dice across the data to derive meaningful insights on the production ML ecosystem! |
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Building Virtual Worlds with ML This is mind blowing: Who would've thought that training Image Diffussion Models on Videogame visuals+player inputs would result in fully ML-generated virtual worlds 🤯 Researchers from Microsoft and Geneva/Edinburgh University have released DIAMOND (DIffusion As a Model Of eNvironment Dreams) - a reinforcement learning agent that leverages diffusion models for world modeling in Atari games, eliminating the need for discretization and reducing mode collapse issues inherent in token-based approaches. DIAMOND enhances the modeling of visual details, and achieves a mean human-normalized score of 1.46 on the Atari 100k benchmark (a new state-of-the-art for agents trained entirely within a world model), and demonstrates that diffusion models can serve as effective drop-in replacements for real environments in reinforcement learning. And the code is open source and available in github - this is an exciting new domain I had not come across, certainly an area to keep an eye into. |
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Repo of ML Monitoring Metrics The Little Book of ML Metrics is an open-source handbook designed for production machine learning practitioners, and it is aiming to build a comprehensive reference to all-things-metrics in ML monitoring. This is a great ambitious project aiming to consolidate a comprehensive list of metrics in ML monitoring across the domains of regression, classification, clustering, ranking, computer vision, NLP, generative AI, probabilistic models, bias, fairness, and business metrics. |
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Salesforce Red Teams for AI/LLMs Salesforce's Responsible AI team presents a framework for reproducible red teaming that tackles the challenges of AI product testing. It is great to see organisations drive forward the discourse on red teaming for AI, Salesforce doubling down on four key areas: 1) High-quality, use case-specific data that is properly stored and maintained for reproducibility; 2) Programmatic access to products via APIs or clients to automate and scale testing efficiently; 3) Clear taxonomies for evaluating outputs to ensure stakeholder alignment and consistent assessments; and 4) Comprehensive test plans to manage expectations and scope technical work effectively. |
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Cognitive Load in Developers One of the biggest challengers to productivity in development is cognitive load, and this is a great resource that dives into key areas to reduce the mental effort needed to understand code. Extraneous cognitive load, caused by overly complex conditionals, excessive small modules or microservices, and unnecessary abstractions, can be reduced by simplifying code, favoring deep modules with simple interfaces, and using language features sparingly. Properly applying principles like Domain-Driven Design (DDD) and avoiding unnecessary complexity ensures that code remains understandable and maintainable, improving productivity and collaboration across teams. |
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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:
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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! |
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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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