One of the great new features that AzureSMR is enabling is the read and write access to Azure Blog Storage. This is happening in a similar manner as is the case for when you use Python.
Shameless copy from the README:
In order to access Storage Blobs you need to have a key. Use azureSAGetKey() to get a Key or alternatively supply your own key. When you provide your own key you no longer need to use azureAuthenticate() since the API uses a diferent authentication approach.
sKey <- AzureSAGetKey(sc, resourceGroup = "Analytics", storageAccount = "analyticsfiles")
To list containers in a storage account use azureListContainers()
azListContainers(sc, storageAccount = "analyticsfiles", containers = "Test")
To list blobs in a container use azureListStorageBlobs()
azureListStorageBlobs(sc, storageAccount = "analyticsfiles", container = "test")
To Write a Blobs use azurePutBlob()
AzurePutBlob(sc, StorageAccount = "analyticsfiles", container = "test",
contents = "Hello World",
blob = "HELLO")
To read a blob in a container use azureGetBlob()
azureGetBlob(sc, storageAccount = "analyticsfiles", container = "test",
Great new package for the people that use Microsoft Azure as their platform of choice and love R. With AzureSMR you are capable to handle the following services:
- Azure Blob: List, Read and Write to Blob Services
- Azure Resources: List, Create and Delete Azure Resource. Deploy ARM templates.
- Azure VM: List, Start and Stop Azure VMs
- Azure HDI: List and Scale Azure HDInsight Clusters
- Azure Hive: Run Hive queries against a HDInsight Cluster
- Azure Spark: List and create Spark jobs/Sessions against a HDInsight Cluster(Livy)
Install it from your interactive shell:
Transparently immersive experiences: Technology will continue to become more human-centric to the point where it will introduce transparency between people, businesses and things. This relationship will become much more entwined as the evolution of technology becomes more adaptive, contextual and fluid within the workplace, at home, and interacting with businesses and other people.
The perceptual smart machine age: Smart machine technologies will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks that will allow organizations with smart machine technologies to harness data in order to adapt to new situations and solve problems that no one has encountered previously.
The platform revolution: Emerging technologies are revolutionizing the concepts of how platforms are defined and used. The shift from technical infrastructure to ecosystem-enabling platforms is laying the foundations for entirely new business models that are forming the bridge between humans and technology.
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In this blog post I talk through an example of how to pick the best model when you deal with these kind of problems. I also touch the subject of cost-sensitive predictions, introducing some code to generate plots that will help you understand your model in cost fashion. Even more important, it will be essential for grasping the full business impact when moving to a data driven world!
#DataScience #R #MachineLearning #AzureML
Evaluating Machine Learning models when dealing with imbalanced classes – Developing Analytics Solutions with the Data Insights Global Practice – Site Home – MSDN Blogs
Sander Timmer, PhD. In real-world Machine Learning scenarios, especially those driven by IoT that are constantly generating data, a common problem is having an imbalanced dataset. This means, we have far more data representing one outcome class than the other. For example, when doing predictive …
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From now one, for each of my blog posts I will document the code in a more useful way by putting it into a Github repository. Hope this helps as the current way of sharing code through MSDN blogs is not easy to replicate.
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When building an automated predictive pipeline, to have periodically batch-wise score new data, there is a need to control for quality of the predictions. The Azure Data Factory (ADF) pipeline will help you ensure that your whole data set gets scored. However, this is not taking into consideration that data can change over time. For example, when predicting churn changes in your website or service offerings could change customer behavior in such a way that retraining of the original model is needed. In this blog post I show how you can use #Jupyter Notebooks in +Microsoft Azure Machine Learning (AML) to get a more systematic view on the (predictive) performance of your automated predictive pipelines.
#DataScience #MachineLearning #Azure #AzureDataFactory #Python #Notebook
Using Azure Machine Learning Notebooks for Quality Control of Automated Predictive Pipelines – Developing Analytics Solutions with the Data Insights Global Practice – Site Home – MSDN Blogs
By Sander Timmer, PhD, Data Scientist. When building an automated predictive pipeline to have periodically batch-wise score new data there is a need to control for quality of the predictions. The Azure Data Factory (ADF) pipeline will help you ensure that your whole data set gets scored.
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