I hope I am not missing something obvious here. I am using the new azure ml studio designer. I am able to use to create datasets, train models and use them just fine.
azure ml studio allows creation of Jupyter notebooks (also) and use them to do machine learning. I am able to do that too.
So, now, I am wondering, can I build my ML pipeline/experiment in ML studio designer, and once it is in good shape, export it as a python and jupyter notebook? then, use it in the same designer provided notebook option or may be use it locally?
This is not currently supported, but I am 80% sure it is in the roadmap.
An alternative would be to use the SDK to create the same pipeline using ModuleStep where I believe you can reference a Designer Module by its name to use it like a PythonScriptStep
The export Designer graph to notebook is in our roadmap. For now, please take a look at the ModuleStep in SDK and let us know if you have any questions.
Thanks,
Lu Zhang | Senior Program Manager | Azure Machine Learning
Here are the instructions to Use the studio to deploy models trained in the designer - Azure Machine Learning | Microsoft Docs and document that explains how we can get access to score.py and conda_env.yaml files under Output + logs tab for Train module.
Related
Basically title. The Azure documentation for v2 is constantly getting updated, and as of now i have no resource to find out how you can register a pre-trained model from SentenceTransformers on AzureML for future use in endpoints. The library is based on Pytorch, but so far I've had no luck in using MLFlow(mentioned in the docs) to register it.
I don't have much code to show, so any help whatsoever would be appreciated.
With MLFlow, you have to first save or log your model before you can register it. But with log_model you can do both in one step
mlflow.pytorch.log_model(model, "my_model_path", registered_model_name="fancy")
Then it is easiest to deploy it from the AzureML Studio:
It would be great if you can shed some light.
I m building a data science project using MLFLOW. I can install most of library through conda.yaml. But there is one library which is in azure artifact. which can not be packaged directly. Is there any way we can mention extra libraries for MLFLOW when we run it on databricks/Locally. Which MLFLOW can use it while running
I'm trying to follow the quide
https://towardsdatascience.com/how-to-train-your-neural-networks-in-parallel-with-keras-and-apache-spark-ea8a3f48cae6#
to try out keras with systemml and spark.
Anyway I could not find the mentioned free spark plan on ibm watson. Can anyone help me to find it or is it just not available anymore?
Thanks!
In your IBM Watson Studio (you can have a free account), go to your projects (or create one), in the project go to "Environments" tab and choose one of your existing environments or create a new environment, it allows you to choose the Spark environment described at the URL that you mentioned.
Thanks!
Acutally that was just the way I was trying out. But I was confused that it says it will consume capasity units per hour and the block said it will be free.
Now I found out, that one has 50 capacities free each month. So I can try the code.
I trained a model in AZURE ML. Now i want to use that model in my ios app to predict the output .
How to download the model from AZURE and use it my swift code.
As far as I know, the model could run in Azure Machine Learning Studio.It seems that you are unable to download it, the model could do nothing outside of Azure ML.
Here is a similar post for you to refer, I have also tried #Ahmet's
method, but result is like #mrjrdnthms says.
I am new to cloud computing. I created a new cloud solution using Visual Studio 2010.
I need to deploy my solution somewhere in order to test it.
As I saw in my researches, I should have an account on http://www.microsoft.com/windowsazure/account/
Currently i do not have an account there, so where should I deploy my application, and how can i test it?
If you go here: http://msdn.microsoft.com/en-us/windowsazure/cc974146.aspx you can download the whole SDK and other tools which essentially allows you to run Azure on your local machine. It requires you to have SQL Server installed.
Apologies for the lack of details, it's quite a while since I did it myself. But, poke around on that page and you'll find all the tools and documenation you need. It's a big, hairy thing to get your head around so you'll need to set some time aside to just read, sadly.
Best resource to get started is the Azure training kit, get it here:
http://www.microsoft.com/downloads/en/details.aspx?FamilyID=413e88f8-5966-4a83-b309-53b7b77edf78&displaylang=en
Watch some videos then dive into the labs, the best teacher is hands on experience.