Production ML Ecosystem 16k ⭐️ Our Production Machine Learing repo continues to grow at a steady pace, recently hitting the milestone of 16k Github Stars ⭐️🚀 This list has grown thanks to the 100+ active contributors who continuously monitor for new tools and frameworks in the machine learning ecosystem. As of today this encompasses over 27 categories ranging across explainable AI, privacy preserving ML, model versioning, monitoring, serving and more. If there's any tools or frameworks missing please do contribute with a pull request as it would be greatly appreciated! |
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A Guide to Visual Transformers A great visual guide to Transformers for image tasks in machine learning: This is one of the best and intuitive visualisations of the transformer architecture for image classification. This guide covers quite a comprehensive overview across data preparation, image patching and flattening, embedding, and the transformation process involving queries, keys, values, attention mechanisms, and positional embeddings. It also discusses the application of multi-head attention, residual connections, and feed-forward networks, including the training using cross-entropy loss. For anyone looking to get their hands dirty you can also try the Colab Notebook for the hands on example using Pytorch. |
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Primer on Hacking LLM Models Hacking Large Language Models for Beginners: As part of the growing adoption of LLMs, practitioners and researchers have found new and innovative ways to exploit machine learning systems involving these. This means that robust security measures are required in production applications that are powered by LLMs, as these models become integral to various applications. Common security vulnerabilities include data exfiltration and model misalignment, this article provides key resources, such as the OWASP and MITRE frameworks, and provides key guidance on approaches organisations are taking such as red teaming for security evaluation of ML systems. |
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Snowflake's Largest LLM Snowflake has introduced a new text-embedding model designed specifically for retrieval applications, boasting an integration of 128 expert sub-models, a notable advancement over current offerings. This release is part of a broader trend where tech companies are increasingly releasing a plethora of open-source models, with Huggingface currently hosting over 600,000. These developments raise questions about the sustainability and economic rationale behind such heavy investments in model training and finetuning, especially considering the high costs associated with the computational resources required, predominantly provided by Nvidia. The strategic intent behind these widespread releases remains unclear, prompting further inquiry into the potential benefits and long-term viability of this approach in the tech industry. |
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Foundational Tips for Engineers A classic for excellent (and humourous) advice to software practitioners - The Grug Brained Developer: Key advice includes embracing the power of saying "no" to unnecessary features to maintain simplicity, utilizing the 80/20 rule to focus on delivering most of the value with minimal complexity, and being wary of adding early abstractions. This classic piece also emphasises the importance of factoring code wisely, advocating for small gradual refactoring, and highlighting the pitfalls of excessive abstractions which often complicate projects unnecessarily. |
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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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