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I am Chiara Lepore, a Research Scientist at LDEO-Columbia University. We briefly met this summer over lunch at the Hail Workshop held in Boulder.
I have seen your tweet about your repo with the ML classes, and I am excited to give it a try.
I am contacting you, right now, to introduce you to a great open source effort brought forward by a large community, called Pangeo. Since you have shared your course (thank you!) I gather you are keen to share your efforts in an open source community, and I think you will find Pangeo very interesting and helpful.
Pangeo is a NSF EarthCube funded project and it is, as of now (it has changed scope in the past year), very much interested in allowing simple Big Data analysis in the cloud, with a specific focus on Geosciences. You can read more about it here on its website and check out the GitHub repo where we use the issue section to communicate and discuss further developments.
I thought about introducing you to Pangeo when I have looked at the section in which you carefully describe how to set up the Jupyter Hub Containers.
In fact, one of the main goal of Pangeo, is to develop cloud computing platforms that are already set up for people, like me for example, who could find complicated to set it up on their own. More over, Pangeo is right now - although it will soon be dismissed - providing a cloud computing platform open to everyone (after they request access through an issue on the repo) to try out Jupyter on the cloud.
Yesterday a new issue was opened in which folks interested in ML are going to discuss ways Pangeo can help simplify the workflow and provide support to people interested in ML. In fact, one of the goals of Pangeo, being a NSF funded proposal, is also to provide open source tutorials, workflows, and anything that can help people spun up and over the initial hump that sometimes setting up environments, dealing with large data, preprocessing etc, can create.
I am not the best person to describe the technical details of Pangeo infrastructure, but I hope you will join us for a chat and that Pangeo can help you with your ML course.
One specific thing we could try is to turn your course into a binder that runs on the pangeo binder service.
The text was updated successfully, but these errors were encountered:
Hello David,
I am Chiara Lepore, a Research Scientist at LDEO-Columbia University. We briefly met this summer over lunch at the Hail Workshop held in Boulder.
I have seen your tweet about your repo with the ML classes, and I am excited to give it a try.
I am contacting you, right now, to introduce you to a great open source effort brought forward by a large community, called Pangeo. Since you have shared your course (thank you!) I gather you are keen to share your efforts in an open source community, and I think you will find Pangeo very interesting and helpful.
Pangeo is a NSF EarthCube funded project and it is, as of now (it has changed scope in the past year), very much interested in allowing simple Big Data analysis in the cloud, with a specific focus on Geosciences. You can read more about it here on its website and check out the GitHub repo where we use the issue section to communicate and discuss further developments.
I thought about introducing you to Pangeo when I have looked at the section in which you carefully describe how to set up the Jupyter Hub Containers.
In fact, one of the main goal of Pangeo, is to develop cloud computing platforms that are already set up for people, like me for example, who could find complicated to set it up on their own. More over, Pangeo is right now - although it will soon be dismissed - providing a cloud computing platform open to everyone (after they request access through an issue on the repo) to try out Jupyter on the cloud.
Yesterday a new issue was opened in which folks interested in ML are going to discuss ways Pangeo can help simplify the workflow and provide support to people interested in ML. In fact, one of the goals of Pangeo, being a NSF funded proposal, is also to provide open source tutorials, workflows, and anything that can help people spun up and over the initial hump that sometimes setting up environments, dealing with large data, preprocessing etc, can create.
I am not the best person to describe the technical details of Pangeo infrastructure, but I hope you will join us for a chat and that Pangeo can help you with your ML course.
One specific thing we could try is to turn your course into a binder that runs on the pangeo binder service.
The text was updated successfully, but these errors were encountered: