Data Quality View of ML A Data-Quality Driven View of MLOps from Microsoft: An insightful research article from Microsoft research that explores the role of data quality in Machine Learning Operations. The insights provided are still relevant today, including four main areas: 1) optimizing ML model quality through strategic data cleaning, 2) managing expectations with realistic feasibility studies, 3) preventing overfitting with innovative continuous integration techniques, and 4) efficiently selecting the best model for new data through continuous quality testing. |
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3Blue1Brown on AI Transformers 3Blue1Brown has released a video covering a comprehensive deep dive into machine learning transformer architectures with top visualisations and content that will prove useful to both new and seasoned practitioners. This video does a fantastic job on breaking down how large language models work under the hood, and how these transformer architectures continue to revolutionise various sub-fields of machine learning. |
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NLP Fundamentals Deep Dive Natural Language Processing Fundamentals - Tokens, N-Grams, and Bag-of-Words Models: Amid the rise of LLMs it is still important to build a strong intuition on the fundamentals in NLP and ML, this series provides a great refresher across the basics of tokens and n-grams. Similarly n-gram models and bag-of-word models are key methodologies which provide their importance in the architecture of autoregressive and autoencoding models like ChatGPT. |
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Cognitive Load in Software Cognitive load is how much a developer needs to think in order to complete a task, understanding this concept can help maximise productivity for the self and others: There are clear detrimental effects of excessive cognitive complexity arising from intricate code structures, inappropriate use of programming paradigms, and excessive reliance on complex language features or frameworks. Advocating for simplicity is important in pertinent contexts through minimizing unnecessary complexity, favoring clear and concise code, and critically evaluating established best practices to reduce cognitive strain. This approach aims to improve developer productivity and reduce errors by making codebases more accessible and easier to understand, and this repo provides a great resource to explore this topic further. |
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How to Think of Eng Quality How to not die by a thousand cuts; or, how to think about software quality: The concept of quality in software development has been explored across many resources and methodologies. This resource provides a great conceptual framework that breaks down linear workflows for their inefficiency in managing risks and feedback, and suggests that the accumulation of small errors ("cuts") can significantly degrade software quality. Approaches to building quality include embracing diverse perspectives, stakeholder collaboration and viewing challenges as opportunities for growth. |
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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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