raghavsb

5 ms·

1.
by raghavsb 2y ago
Great, I landed on the reasoning and citations bit through trial and error and the outputs improved for sure.
2.
by raghavsb 8y ago
rorodata ( https://rorodata.com ) | Hyderabad, India | Software Engineers | Full-time | Onsite In ML/DL, the real modelling effort starts in production. But deploying models in production, at scale is hard - there are signifi
3.
by raghavsb 8y ago
Couldn't agree more on the data size. In most cases beefy machine work. Would on-demand (cloud) make it simple? Also beefy machine works for training jobs. But we need to deploy the models too.
4.
by raghavsb 8y ago
Smaller datasets will work on laptop/desktop. For DL work with large datasets you need to build a GPU workstation. Moreover there setting up environment and dependencies on different hardware setups is not straight forward. Cloud provi
5.
by raghavsb 8y ago
The talk title is quite provocative but the material discussed not so much. It is true that core data science is iterative, needs to reproducible and more recently explainable. Can you do everything in GUI? It really depends on where you se
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Show HN: Rorolite and firefly – DL models as REST APIs, don't write Flask apps
2 points by raghavsb 9y ago|0 comments
7.
by raghavsb 9y ago
Isn’t this more like Clarifai and comparables? Scikit and Keras are still lower levels of abstraction
8.
by raghavsb 9y ago
One of the collaborators on the project. Keen to get some feedback.
9.
Show HN: rorolite - CLI tool to deploy ML apps to your own server(github.com)
5 points by raghavsb 9y ago|1 comments