The State of Responsible AI What are the most pressing issues on ML Governance? What about the most important concepts of Responsible AI? Our keynote on Responsible AI is now live at the Technical University Munich's Institute for Ethics in AI page 🚀 In this session we navigated the current landscape of responsible AI, focusing on the industrial, organizational, and technical aspects crucial for successful AI deployment, including governance challenges, accountability, security concerns, infrastructure complexities, risk mitigation, and building scalable, reliable AI systems that drive innovation while adhering to responsible practices. Check out the full video and slides! |
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Production LLM Usecase List ZenML dropped a massive new database of LLMOps usecases with 300+ curated generative AI and LLM implementations in the real world: It is important for practitioners to go beyond the hype when it comes to LLMs, and concrete / practical examples in industry can provide an actionable guidance - this includes architectural choices, tooling stacks, evaluation methodologies, and operational best practices. This provides quite an interesting perspective on choices for handling RAG, monitoring, frameworks choice (e.g. LangChain), etc. |
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Google's 5-Day GenAI Course Google has released a self-paced 5-day intensive course on GenAI foundations with a structured approach to modern generative AI workflows: This is quite a comprehensive practical resource covering foundational models and prompt engineering, embeddings and vector databases, generative agents, domain-specific LLMs, and finally MLOps for generative AI. This is a good opportunity for practitioners to gain hands-on experience through whitepapers, code labs, and expert-led discussions on Kaggle and YouTube. |
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DeepMind World Foundation AI DeepMind announces an exciting large-scale "GenAI world model" for creating and simulating interactive 3D environments from a single prompt image. In practice, this means ML practitioners can quickly create an endless variety of interactive 3D settings to train and stress-test agents in simulated environments like games. This model operates as an autoregressive latent diffusion system trained on extensive video data, enabling it to produce coherent, action-responsive virtual worlds that can be explored with standard keyboard and mouse controls. This is quite an exciting area of research, particularly as we have seen papers also exploring the interactions of agents at scale across virtual worlds. |
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MSFT Quantifying Bad Days Microsoft demystifies the concept of "Bad Days" in development through a quantitative and qualitative study of main drivers and broader impact in productivity and beyond: Microsoft is known for publishing interesting research in developer productivity (e.g. SPACE framework, etc), and this time they provide insightful results breaking down main drivers of "bad days" including slow builds, long pull request delays, excessive meetings, unclear processes, poor documentation, and difficult team dynamics. These “bad days” lead to stress, reduced morale, and even career dissatisfaction - by correlating self-reported “bad day” factors with system-level metrics like build and PR times, the study provides evidence-based validation of developer concerns. For ML practitioners in production environments, this work can be quite interesting to identify the specific areas where investments can be made to improve overall productivity and developer happiness. |
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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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