Open Source $8 Trillion Economic Value Harvard researchers found that the value of open source globally is over $8.8 trillion dollars and growing at massive speed: This is obviously partially a result of its widespread usage unsurprisingly across industry's most critical codebases, with OSS embedded in 96% of all codebases worldwide. This study really reminds us of the importance of OSS across society, where companies would have to spend approximately 3.5 times more on software if OSS did not exist. The study breaks down the value of OSS into two aspects: the cost to develop OSS and demand-side cost to replace OSS if it were to disappear, and shows reveals that only 5% of developers responsible for 93% of the supply-side value. This is an important reminder for policymakers to support OSS development considering the growing critical role in the global economy. |
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Google Gemini, Wisk & Veo2 Google DeepMind has been opening access to mindblowing text-to-video features with the release of Veo 2: Gemini is now allowing users to describe scenes to create cinematic-quality video clips at 720p resolution in MP4 format with mind blowing outputs. This model leverages advanced understanding of real-world physics and human motion with features like Whisk Animate to convert images into eight-second videos. These type of features are improving at lightning speed and are clearly going to have a large impact on creative industries for the years to come, so it will be critical to also ensure best practice and responsible AI standards are in place across. |
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DeepSeek's Distributed File System An interesting introspective deep dive into China's DeepSeek Fire-Flyer File System, an open-source distributed filesystem designed to provide scalable and fault-tolerant storage for LLM usage: This system abstracts the complexities of distributed data storage and allowing users to interact with it as if it were a local filesystem, including metadata management, Mgmtd for cluster management, Storage for physical data storage, and Client for file operations. The system utilizes the CRAQ (Chain Replication with Apportioned Queries) protocol to ensure data consistency, though this can introduce write latency due to its sequential nature. It is interesting to see how the tech race is also leading to the release of not only LLMs but also the infrastructure that powers them, which is great for ML practitioners across the board. |
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TikTok's LLM Reinforcement Learning TikTok releases their reinforcement learning framework to enhance LLMs for a range of usecases such as code interpreter interpreters, problem-solving, structured reasoning and more: It is quite exciting to see large tech companies opening the black box on key knowledge beyond network architectures that allows for the high quality reasoning capabilities that we see across models as of today, such as invoking tooks based on feedback, triggering efficient retrieval mechanisms, etc. ReTool trains models to autonomously refine their tool usage strategies through a combination of cold-start data generation and RL training, claiming to outperform text-based RL models in both accuracy and efficiency. Definitely an interesting space to keep an eye on as it progresses given the attention and active contributions from tech giants across the board. |
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Lessons from the Best Programmers Key one-liner lessons from the best programmers: 1) Read the Reference; 2) Know Your Tools Really Well; 3) Read The Error Message; 4) Break Down Problems; 5) Don’t Be Afraid To Get Your Hands Dirty With Code; 6) Always Help Others; 7) Write; 8) Never Stop Learning; 9) Never Stop Learning; 10) Build a Reputation; 11) Have Patience; 12) Never Blame the Computer; 13) Don’t Be Afraid to Say “I Don’t Know”; 14) Don’t Guess; 15) Keep It Simple. The is a great article as it provides us practitioners a reminder on the time-tested best practices to master tools and contributions. |
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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 2025:
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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