Ịmụta Ihe n'Ụgbọ
Nnọọ na ibe mbụ nke otu Wikimedia Foundation's Machine Learning.
Ọmụmụ igwe na Wikimedia Foundation
Our team oversees the development and management of machine learning models for end users, as well as the infrastructure required for designing, training, and deploying these models.
- Machine Learning Model Cards
- The Machine Learning Modernization Project
- Nnukwu nku - Ihe nlereanya mmụta igwe na-arụ ọrụ na Kubernetes site na iji KServe.
For archived projects, see this list.
Nwere ajụjụ? Chọrọ ịgwa ndị otu ma ọ bụ obodo ndị ọrụ afọ ofufo anyị gbasara mmụta igwe? Nke a bụ ụzọ kachasị mma iji jikọọ anyị.
Gịnị bụ ihe ọhụrụ?
- GPU order is underway. We are in the process of ordering a series of servers to use for training and inference. Each server will have two MI210 AMD GPUs. Most will be reserved for model inference (specifically, larger models like LLMs), but we will use two servers (4 GPUs) to create a model training environment. This model training environment will start very small and scrappy but will hopefully grow into a place for automated retraining of models and the standardization of model training approaches. The next steps are a single server will on its way to our data center, once this is tested we will make the full order.
- Work on caching for Lift Wing continues. We have in the process of making a large order of GPUs. However, to optimize our resource use, one of the best strategies we can do is conduct model inference using our existing CPUs. This is not always possible, for example cases when the set of possible model inputs is not finite. However, in cases where the possible inputs are finite we can cache the predictions for those inputs and then serve them to users rapidly with minimal compute used. This is a similar system to that which was originally used on ORES.
- The pentesting of Lift Wing continues. The testing is being done by a third party contractor and is examining our vulnerability to malicious code.
- Wikimedia's branding team has come out with some suggestions for the naming of machine learning tools and models. The hope is that our naming is more systematic and less ad-hoc.
- Chris helped organize and attend an event in Bellagio, Italy to craft a research agenda for researchers interested in Wikipedia. That research agenda is avaliable here.