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Issue #251🤖 
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The ML Engineer this week:
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The state of production ML in 2023: As the number of production machine learning use-cases increase, we find ourselves facing new and bigger challenges where more is at stake. Because of this, it's critical to identify the key areas to focus our efforts, so we can ensure our machine learning pipelines are reliable and scalable. In this talk we dive into the state of production machine learning in the Python Ecosystem, and we will cover the concepts that make production machine learning so challenging, as well as some of the recommended tools available to tackle these challenges.
LinkedIn is enhancing its recommendation and search systems using Embedding based retrieval, a method that identifies relevant items based on their proximity in an embedding space. This technique captures the contextual intent of search or recommendation requests, ensuring geometrically close matches in the embedding space are retrieved. To support this, LinkedIn has developed new infrastructure components and introduced composite and multi-task learning models, streamlining the process of capturing user interests and delivering more personalized experiences.
One of OpenAI ChatGPT core engineers shared insightful views in a recent presentation at Seoul National University on "Large Language Models (in 2023)". This highlights the evolving nature of LLMs, emphasizing the need for a perspective shift as abilities emerge at larger scales. The technical intricacies of scaling Transformers were discussed, emphasizing efficient matrix multiplications across multiple machines. An interesting perspective identified the maximum likelihood objective function as a potential bottleneck and advocated for a paradigm shift towards learning this function with a more expressive neural network, emphasizing the importance of first-principles understanding in this rapidly advancing field.
OnnxStream is a specialized inference library designed to run large transformer models, particularly Stable Diffusion 1.5 and XL 1.0, on low-memory devices like the Raspberry Pi Zero 2. By focusing on minimizing memory consumption, OnnxStream can operate with up to 55x less memory than OnnxRuntime. The repository offers techniques like "attention slicing" and quantization to achieve these results and provides detailed build instructions for various platforms. This tool is ideal for ML practitioners aiming to deploy heavyweight models on memory-constrained devices.
A comprehensive comparison of leading vector databases in 2023, emphasizing their pivotal role in semantic search and retrieval-augmented generation. Among the databases analyzed, including Pinecone, Weviate, Milvus, Qdrant, Chroma, Elasticsearch, and PGvector, Milvus stands out in performance and community strength, while Pinecone shines for its developer experience and hosted solution. The ideal choice varies based on specific needs, with the author favoring Pinecone and Milvus for their performance, community engagement, and pricing flexibility.
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.
Relevant upcoming MLOps conferences:
Open Source MLOps Tools
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!
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!
© 2018 The Institute for Ethical AI & Machine Learning