I created an Azure ML dataset with a single file inside a storage blob container. Azure ML studio portal then showed 1 file in the dataset version 1.
I wanted to add 2 more files and create a new dataset version. So I copied 2 more files to the same blob container folder. Surprisingly even before I created a new dataset version, the ML studio portal UI shows the number of files in the same dataset as 3. (image below).
I then went through Azure ML versioning docs which tell datasets are just references to original data. I also see a suggestion to create new folders for new data and I agree that the new files were not copied to a new folder here as recommended.
But still, the metadata (e.g. files in dataset, total size of dataset etc) of a previously created dataset version is getting updated. What is the importance of Azure ML dataset versioning if metadata of dataset version itself is being updated?
A related question was in SO, but closed as a bug.
Versioning will improve the accuracy of the model. Based on the data extracted we can perform the prediction model running on different versions of the dataset. The dataset may consist of the same name, but the version will contain different values. This supports the parallel execution of the models on the same storage account support.
We can create different Auto ML prediction models with different versions of the dataset.
The two versions are uploaded to the same blobstorage and now using any one of the version, I will run the prediction model (Classification).
The above screen is of churn_analysis running as the Auto ML prediction model, running with 25% of testing and 75% of training the dataset. The Version of the dataset used in this prediction model is mentioned in the below image.
In the same manner we can do the prediction model with different versions of the training and test set splits and also the type of model for each version can be chosen. We will get different models result on the single dataset for better understanding of the data.
Related
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’m performing a series of experiments with Azure AutoML and I need to see the featurized data. I mean, not just the new features names retrieved by method get_engineered_feature_names() or the featurization details retrieved by get_featurization_summary(), I refer to the whole transformed dataset, the one obtained after scaling/normalization/featurization that is therefore used to train the models.
Is it possible to access to this dataset or download it as a file?
Thanks.
Microsoft expert confirmed that currently they "don't store the dataset from scaling/normalization/featurization after the run is complete". Answer here.
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
We are using Azure Machine Learning Studio for building Trained Model and for that we have used Two Class Bayes Point Machine Algorithm.
For sample data , we have imported .CSV file that contains columns such as: Tweets and Label.
After deploying the web service, we got improper output.
We want our algorithm to predict the result of Label as 0 or 1 on the basis of different types tweets, that are already stored in the dataset.
While testing it with the tweets that are there in the dataset, it gives proper result, but the problem occurs while testing it with other tweets(that are not there in the dataset).
You can view our experiment over here:
Experiment
Are you planning to do a binary classification based on the textual data on tweets? If so you should try doing feature hashing before doing the classification.