I am creating Azure ML experienment to predict multiple values. but in azure ml we can not train a model to predict multiple values. my question is how to bring multiple trained models in single experienment and create webout put that gives me multiple prediction.
You would need to manually save the trained models (right click the module output and save to your workspace) from your training experiment and then manually create the predictive experiment unlike what is done in this document. https://learn.microsoft.com/en-us/azure/machine-learning/studio/walkthrough-5-publish-web-service
Regards,
Jaya
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I am currently training a model using an azure ML pipeline that i build with sdk. I am trying to add cross-validation to my ml step. I have noticed that you can add this in the parameters when you configure the autoML. My dataset consists of 30% label 0 and 70% label 1.
My question is, does azure autoML stratify data when performing the cross-validation? If not i would have to do the split/stratify myself before passing it to autoML.
Auto ML can stratify the data when performing cross-validation. The following procedure needs to be followed to perform cross-validation
Create the workspace resource.
After giving all the details, click on create
Launch the Studio and go to AutoML and click on New Automated ML job
Upload the dataset from here and give the basic details required.
Dataset uploaded with some basic categories
After uploading dataset use that dataset for the prediction model performance
Here for prediction, we can choose the k-fold cross validation for validation type and number of cross validations as 5. There is no split we are performing. The model will perform according to the validation requirements.
I just finished training a Custom Azure Translate Model with a set of 10.000 sentences. I now have the options to review the result and test the data. While I already get a good result score I would like to continue training the same model with additional data sets before publishing. I cant find any information regarding this in the documentation.
The only remotely close option I can see is to duplicate the first model and add the new data sets but this would create a new model and not advance the original one.
Once the project is created, we can train with different models on different datasets. Once the dataset is uploaded and the model was trained, we cannot modify the content of the dataset or upgrade it.
https://learn.microsoft.com/en-us/azure/cognitive-services/translator/custom-translator/quickstart-build-deploy-custom-model
The above document can help you.
I want to train the model with binary logistic regression model,with a dataset of 3000 data points. while creating the pipeline , it fails at the training model step.
Please help me in training the model with large dataset or retrain the model continuously.
Also Do pipelines have any limitation on the dataset? if so, what is the limit
I haven't seen there is a limitation for training dataset size. May I know how you do the pipeline? If you are using Azure Machine Learning Designer, could you please try the enterprise version? https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines#building-pipelines-with-the-designer
Also, I have attached a tutorial here for large data pipeline: https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification
We use Azure Custom Vision service to detect products on store shelves. This works pretty well, but we can't understand why each subsequent iteration of training makes the forecast worse. Does the service create a model from scratch in each iteration, or does it retrain the same model?
Whether you're using the classifier or the object detector, each time you train, you create a new iteration with its own updated performance metrics.
That means that each iteration in a project is independent, built on different sets of training images.
To maintain high performance don't delete existing images from the previous iteration before retraining, because by retraining you're basically creating a new model based on the images you currently have tagged.
It is also stated in the documentation here for the classifier, and here for the object detector.
So I have been playing around with Azure ML lately, and I got one dataset where I have multiple values I want to predict. All of them uses different algorithms and when I try to train multiple models within one experiment; it says the “train model can only predict one value”, and there are not enough input ports on the train-model to take in multiple values even if I was to use the same algorithm for each measure. I tried launching the column selector and making rules, but I get the same error as mentioned. How do I predict multiple values and later put the predicted columns together for the web service output so I don’t have to have multiple API’s?
What you would want to do is to train each model and save them as already trained models.
So create a new experiment, train your models and save them by right clicking on each model and they will show up in the left nav bar in the Studio. Now you are able to drag your models into the canvas and have them score predictions where you eventually make them end up in the same output as I have done in my example through the “Add columns” module. I made this example for Ronaldo (Real Madrid CF player) on how he will perform in match after training day. You can see my demo on http://ronaldoinform.azurewebsites.net
For more detailed explanation on how to save the models and train multiple values; you can check out Raymond Langaeian (MSFT) answer in the comment section on this link:
https://azure.microsoft.com/en-us/documentation/articles/machine-learning-convert-training-experiment-to-scoring-experiment/
You have to train models for each variable that you going to predict. Then add all those predicted columns together and get as a single output for the web service.
The algorithms available in ML are only capable of predicting a single variable at a time based on the inputs it's getting.