Google Releases Gemma 2 Google releases the second generation of their Gemma model, bringing significant performance providing models that simplify productionisation with the more compact 9B and 27B models. In this release google focuses on inference efficiency ensuring execution works on single a GPU or TPU, and prioritise integration with Hugging Face and TensorFlow. Google also highlights the importance of responsible AI in context of LLMs, and dive into some of their approach to safety features and resources such as the Gemma Cookbook and the LLM Comparator. |
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Learnings from 900 OSS AI Tools Chip Huyen shares an in-depth analysis of 900 popular open source AI tools, and highlights key learnings across infrastructure, model development, and application development layers. The study is based on 900 GitHub repositories with at least 500 stars, and it reveals a surge in AI applications post-2023 due to tools like Stable Diffusion and ChatGPT, with contributors dominating from both Western and Chinese developers. Some key trends showcase the fast-paced evolution (+ short-lived hype) of new tools, the collaborative nature of the community, and the growing divergence in China's AI landscape. |
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LLMs for Compiler Optimisation Meta introduces their Large Language Model Compiler , designed for code and compiler optimization. This model compiler is built on Code Llama and trained on 546 billion tokens of LLVM-IR as well as assembly code, and is available in 7B and 13B parameter sizes. These models claim to enhance the understanding of compiler behaviors, and showcase results that suggest they significantly outperform previous models like GPT-4 Turbo in tasks such as flag tuning and disassembly. |
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From Baremetal to 70b Model The Imbue Team shares their experience of building and training a 70 billion parameter model on their own infrastructure surpassing GPT-4 in reasoning tasks. They detail the entire setup process: from provisioning machines and installing the OS to resolving hardware issues and ensuring network reliability with InfiniBand. |
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Lessons from 15 Years of Coding Key lessons from 15 years of programming experience: A great resource that provides learnings from a life-long journey in the programming ecosystem. Some of the key learnings revolve enhancing programming efficiency, addressing recurring issues directly, balancing quality with speed based on context, mastering your tools, simplifying unnecessary complexities, fixing bugs at their root, utilizing version history for debugging, embracing imperfect code for feedback, streamlining debugging processes, leveraging team knowledge by asking questions, and prioritising frequent, efficient code shipping. For machine learning practitioners, these strategies can significantly boost productivity and reduce bugs in your machine learning projects as well. |
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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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