Ways to Deploy an ML Model The many ways to deploy a machine learning model from the team at Outerbounds: A great overview for production ML practitioners on the diverse deployment strategies for ML/AI models, focusing on the critical considerations of scale, reliability, and iteration speed. It is often key to consider the broad range of technical requirements based on the application needs - i.e. batch vs real time, reusable vs specialized, modalities, etc. Great practical examples and conceptual frameworks to identify the most suitable deployment approach. |
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xLSTM Innovation in ML xLSTM introducing the Extended Long Short-Term Memory as a challenger for the ever growing wave of transformer architectures: European innovation introduces an advanced version of the traditional LSTM model by incorporating exponential gating and innovative memory structures to address its limitations. These modifications allow xLSTMs to perform competitively with contemporary Transformer models in language processing tasks, showcasing improved capability in handling complex memory operations and scaling to large model architectures. |
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Llama3 Implemented in NumPy Llama 3 implemented in pure NumPy - what better way to learn a concept than by implementing it: Great practical deep dive implementing Llama 3 model using only NumPy, demystifying the underlying model's architecture and nuances. Some of these include key components such as RoPE positional encoding, RMSNorm, and Scaled Dot-Product Attention, alongside optimizations like KV Cache for efficiency. This implementation serves as a great learning resource for machine learning practitioners interested in understanding the details under the hood. |
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META Scaling Law for RecSys META AI Research presents a scaling law for large-scale Recommendation Systems: As recsys grow in adoption and presence across tech companies, the need grows for a scaling law similar to those observed in large language models to understand relationships between considerations such as resources and limits. This design enables the model to effectively and efficiently scale by capturing any-order feature interactions through progressively deeper and wider layers. |
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GPUs Go Brrr GPUs Go Brrr - or how to optimize AI compute on GPUs: Practical strategies and philosophical shifts are necessary to maximize hardware utilization in the world of machine learning compute. Some key best practices include leveraging asynchronous matrix multiplication instructions in GPUs directly from shared memory, and managing the quirks of shared memory to minimize latency and bank conflicts. This article provides an intuitive deep dive into how to streamline complex CUDA kernel programming, making it more accessible and efficient for developers working with intricate ML algorithms. |
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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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