Side projects or working on other people's side projects. The most important things is to connect with the community and learn the technical language to speak with them. This is a relatively small community and you need bunch of different stuff to get started, some ML, coding for sure, some knowledge about how modern accelerators work, some skills to read and understand papers in this direction.
Little impact on the revenue side while cost can be cut significantly.
Many companies have proved it to be useful and their stock prices soar. Why don't we try it
I think the same thing happens to Chinese, too.
Many people are entering from the boarder.
US has a huge issue in the border control on the south side.
Yes. But some of the algorithms cannot benefit that much from the GPU. In my field -- mathematical optimization, lots of algorithms rely on sparse matrix operations and takes many iterations until convergence.
TRAC is a serverless framework that can rapidly convert a data science solution developed locally in the form of jupyter notebook or python script, to an interactive web application that can be used by external stakeholders.
It is extremely useful to solve the “last mile” prod problem for internal DS solutions.
It provides a spreadsheet like UI for users to work with, also provides APIs to interact with the data so more complex UI can be built on top.
The invocation of the computation can be run locally or on kubernetes for now.
Users can provide multiple datasets, run the notebook with different parameters and conduct scenario analysis.
All the things are done automatically by scanning through the notebook provided. Data schemas, Parameters are inferred. UIs and APIs are auto-generated.