Airbnb's New OSS Feature Store Airbnb has released their ML feature store framework as an open source project together with key partners and users such as Stripe: Chronon simplifies the management and integration of data for machine learning practitioners by providing tools that handle both batch and streaming data, support multiple data sources whilst ensuring low latency in feature serving. This is an area where feature stores tend to fall short, allowing ML features to be defined once and used both for offline training and online inference. They also tackle common challenges such as data observability, quality monitoring, and management / governance at scale. |
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ML Books for Engineers If you are looking for a book to expand your ML knowledge in 2024, check out this great list on ML books for Engineers: A great set of 11 essential titles across four categories: Machine Learning & Algorithms, Mathematics & Statistics, Data Science & Analysis, and Tools & Frameworks. The curated selection of books focuses on practical machine learning applications using popular Python frameworks such as Pytorch, Tensorflow, Sklearn, etc, and is designed to equip software engineers with a pragmatic understanding of machine learning technologies and theoretical knowledge, suitable for both newcomers and those expanding their expertise. |
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300 ML Systems Design Usecases 300 practical usecases of ML system design: A comprehensive overview of how major companies like Netflix, Airbnb, and Doordash utilize best practices in machine learning design and operations to enhance their products and operational use-cases. This resource from EvidentlyAI compiles 300 case studies across over 80 companies, focusing on practical ML applications and insights into system design. |
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Half Billion GPT Token Lessons Lessons learned after a half-billion GPT tokens: Great lessons learned from using OpenAI's language models at scale to process over 500 million tokens in a B2B context. Key lessons include the effectiveness of simpler prompts, the sufficient utility of basic chat functions over more complex API features, the positive impact of variable-speed typing on user experience, challenges with handling null outputs and limited response sizes, and the limited relevance of vector databases for small-scale applications. |
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The Lifecycle of an AI Copilot The lifecycle of a production-grade AI code assistant to generate code completions: A great insight on what goes through the nuances of AI code completion. This is covered across 4 stages: 1) Planning, where the code context is analyzed to set the approach; 2) Retrieval, which collects relevant code snippets and contextual data; 3) Generation, where the LLM produces the code based on the provided context; and 4) Post-processing, where the generated code is refined and filtered to ensure relevance and quality. A great resource that highlights the complexities involved in developing an AI system that not only generates code but also integrates deeply with user expectations and sophisticated language understanding tools |
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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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