11–20 of 65 posts
How does it handle data storage? Could we use CometML to store our continuously growing set of labeled data or is there a smart way to link it to Google Cloud Platform?
Can you talk about how CometML fits into the real world state of ML training tracking, which is a pretty terrible Wild West of non-reproducible practices and processes? There have been articles and comments here on HN about the sorry state of ML trackability, with papers being published on models whose training is not reproducible because no one really knows how it was trained. One in particular (I apologize for not…
In such complex cases there's still a little discipline required on the user behalf but overall it's just including a few calls to our Experiment object.
Great to see ML "governance" work being done on the training-part of the pipeline. Seems like this provides a Domino Data Labs based dashboards but without the walled garden environment. I've yet to see similar great initiatives also tackling the deployment-part. E.g. something similar you can stick on top of your model's API (or scheduled batch predictive outputs), as well as incoming instances, to monitor usage pat…
As for monitoring production models that's something we're also working on. It was important to get the training part out first so we can measure those distributions changing.
I have been anxiously waiting for something like this! Very excited to try it out. How does it handle data storage? Could we use CometML to store our continuously growing set of labeled data or is there a smart way to link it to Google Cloud Platform?
Hi, I’m one of the founders of Comet.ml. We built comet.ml to allow machine learning teams to automatically track their machine learning code, experiments, hyperparameters and results. We think that reproducibility is really important so we’re also giving free access to students, academics and open source projects. Feedback is welcome. Ask me anything.
Hi, I’m one of the founders of Comet.ml. We built comet.ml to allow machine learning teams to automatically track their machine learning code, experiments, hyperparameters and results. We think that reproducibility is really important so we’re also giving free access to students, academics and open source projects. Feedback is welcome. Ask me anything.
Looks awesome. I'd love to give it a try with my team. Would you be open to extending the free teams plan to high tech nonprofits in addition to the access you provide to students, academics, and open source projects? We tend to work on distributed projects frequently with industry experts doing pro bono work for us, and something like comet could simplify our collaboration. The size of these pro bono project teams t…
Hi, I’m one of the founders of Comet.ml. We built comet.ml to allow machine learning teams to automatically track their machine learning code, experiments, hyperparameters and results. We think that reproducibility is really important so we’re also giving free access to students, academics and open source projects. Feedback is welcome. Ask me anything.
I would like to use it but I think the price doesn't justify the tool. For a team of 5 people github is $25 a month, you are $745 a month. I do understand a price a bit higher that github but not 30 times more expensive.