META's Text-to-3D-Asset Model META releases an impressive Text-to-3D-Asset AI model: Meta comes back with an updated version of 3DGen, consisting of a cutting-edge pipeline for generating 3D assets from text with high prompt fidelity and high-quality outputs. The system supports physics-based rendering for realistic lighting, and enables retexturing of 3D shapes. Meta had previously released "3D AssetGen" and embeds it in this current release for text-to-3D generation together with their "3D TextureGen" model for text-to-texture generation, which has enabled them to outperform some of the current industry standards visual quality for models in this space. |
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Sequoia on AI’s $600B Question Sequoia Capital takes on AI hype by bringing up the elephant in the room of high $500b compute costs, highlighting the ROI required to break even: Sequoia's David Cahn dives into the growing gap between AI infrastructure investments and revenue, now estimated at $500 billion annually. Indeed compute costs, such as investments in Nvidia's GPUs are crucial, however the costs keep piling up and the revenues need to match these in order to match its potential. Quite an interesting post which provides a current view on the status quo, namely with OpenAI dominating AI revenue - indeed with other few startups trailing, but with the speculative frenzy in AI investment posing return-on-investment risks akin to historical tech bubbles. |
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Uber Modernising Batch & AI Infra Uber is migrating its batch data infrastructure from an on-prem massive-scale (exabyte-scale) Hadoop system to a cloud provider, and is sharing their learning and best practices. In the post they outline some of their motivations as well as approach and key principles. Migrations are a multi-year endevour, indeed interesting to see that Uber has made a public announcement in the beginning stages of their migration - this will certainly be a key resource to keep an eye on as it develops! |
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Machine Learning Operations MLOps continues to develop as an emerging field - the ml-ops.org website continues to be a great resource to find best practices and resources for practitioners looking to develop further knowledge in this space. The ML-Ops.org page provides great insights across model lifecycle management with traditional software engineering, including structured frameworks, principles, concepts and best practices across reproducibility, automation, and CI/CD, between many others. Do check it out. |
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Runway's Text-to-Video Gen v3 AI Video generation continues to blow our minds - this last week we saw Runway's Gen-3 Alpha breaking through with high fidelity multimodal video generation. AI generated video only continues to get better and better - this model supports text prompts as well as image and video inputs to steer the generaiton of the scenes and transitions. It's interesting to see the required collaboration of scientists, engineers, and artists - although quite a competitive spce, it's certainly one to keep an eye on as we can be sure new (competing) models will continue to pop up across various different organisations and platforms. |
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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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