The State of Prod ML in 2024 Our talk on the main stage of WeAreDevelopers is out! This is a fresh view on the State of Production Machine Learning, highlighting key trends and opportunities for 2024: This year's edition on the State of Prod ML dives into the transition of AI models into complex data-centric systems that integrate deeply with organizational processes. We dive into the growing complexity of the machine learning ecosystem as well as how to navigate the growingly complex ecosystem of tools, ensuring robust security, advanced monitoring, and evolving roles within organizations to manage this complexity. We also dive into the impact of emerging compliance, which brings responsible AI deployment to the center. |
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PyCon US Videos are Out PyCon US 2024 videos are out! As always there's a lot to catch up on, particularly for ML & Data practitioners, spanning across sessions on deep learning with PyTorch, building FPGA-based ML accelerators, AI document processing, improving ML reproducibility, advanced memory management techniques and many more. These videos are great resources for any practitioner working in the ML and data science space. |
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Pop Culture in the Age of AI A great write-up analysing the hype in AI, challenging specifically the return-on-investment in generative AI hype. Recently we saw the Goldman Sachs report highlighting "Too Much Spend, Too Little Benefit?", with doubts on the economic viability and productivity benefits of generative AI based on costs, power demands, and limited real-world impact. |
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PapersWeLove in CompSci One key advice for software practitioners looking to take their career to the next level is to read research papers! More specifically, dive into foundational resources that have changed the state of the development ecosystem - and the github repo on "Papers We Love"has a massive curated collection of key computer science research papers, organized by themes. These can't be recommended enough as it contains relevant topics across machine learning, distributed systems, cryptography, and programming languages. |
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Seeing Theory: Probability & Stats Really awesome (and visually pleasing) introduction to probability and statistics. "Seeing theory" is a short course that introduces basic probability, compound probability, probability distributions, frequentist inference, bayesian inference and regression analysis. This resource came out a while back but it's still providing quite an intuitive and visual view on an important topic. |
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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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The Institute for Ethical AI & Machine Learning
