I have created a data science neural network in Python anaconda Spyder. My projects have multiple .py files and I have trained this model for 10 days and weights are generated. I... moreI have created a data science neural network in Python anaconda Spyder. My projects have multiple .py files and I have trained this model for 10 days and weights are generated. I want to use this model as a service and want to deploy it to Azure for consumption. I tried following but facing difficulties -
1) I tried deploying this as "Execute Python Script" in Azure ML studio but I am not finding an option to upload the weights. I understand I can zip all the .py files but what about the trained weights and virtual environment (I am using an old version of tensorflow)?
2) I am seeing an option of creating a Jupyter notebook but my project is created in Spyder and doesn't have .ipynb files. Is there any way to convert my .py files into .ipynb files? ALso, I have created a virtual environment with the older version of tensorflow and python version? How to take care of this while deploying to azure?
3) I tried deploying this to azure as a python web app but again what shall I do with the virtual... less
I want to automate deploying OVA image on VSphere with python. I looked up at some packages viz. Pysphere, psphere but didn't find direct method to do so. is there any Library I'm... moreI want to automate deploying OVA image on VSphere with python. I looked up at some packages viz. Pysphere, psphere but didn't find direct method to do so. is there any Library I'm missing or is there any other way to deploy OVA/OVF files/templates on VSphere with Python. Pls help!!!
There doesn't seem to be too many options for deploying predictive models in production which is surprising given the explosion in Big Data.
I understand that the open-source PMML... moreThere doesn't seem to be too many options for deploying predictive models in production which is surprising given the explosion in Big Data.
I understand that the open-source PMML can be used to export models as an XML specification. This can then be used for in-database scoring/prediction. However it seems that to make this work you need to use the PMML plugin by Zementis which means the solution is not truly open source. Is there an easier open way to map PMML to SQL for scoring?
Another option would be to use JSON instead of XML to output model predictions. But in this case, where would the R model sit? I'm assuming it would always need to be mapped to SQL...unless the R model could sit on the same server as the data and then run against that incoming data using an R script?
Any other options out there? less