- parity or improvement on perf, for both CPU & GPU mode
- better support for learning (fit->transform) so we can embed billion+ scale data
- expose inferred similarity edges so we can do interactive and human-optimized graph viz, vs overplotted scatterplots
New frontiers:
- alignment tooling is fascinating, as we increasingly want to re-fit->embed over time as our envs change and compare, eg, day-over-day analysis. This area is not well-defined yet common for anyone operational so seems ripe for innovation
- maybe better support for mixing input embeddings. This seems increasingly common in practice, and seems worth examining as special cases
Always happy to pair with folks in getting new plugins into the pygraphistry / graphistry community, so if/when ready, happy to help push a PR & demo through!
A few things
Table stakes for our bigger users:
- parity or improvement on perf, for both CPU & GPU mode
- better support for learning (fit->transform) so we can embed billion+ scale data
- expose inferred similarity edges so we can do interactive and human-optimized graph viz, vs overplotted scatterplots
New frontiers:
- alignment tooling is fascinating, as we increasingly want to re-fit->embed over time as our envs change and compare, eg, day-over-day analysis. This area is not well-defined yet common for anyone operational so seems ripe for innovation
- maybe better support for mixing input embeddings. This seems increasingly common in practice, and seems worth examining as special cases
Always happy to pair with folks in getting new plugins into the pygraphistry / graphistry community, so if/when ready, happy to help push a PR & demo through!