AI News, Quickstart: Install and get started with Azure Machine Learning services
- On Wednesday, June 6, 2018
- By Read More
Quickstart: Install and get started with Azure Machine Learning services
Azure Machine Learning services (preview) are an integrated, end-to-end data science and advanced analytics solution.
It helps professional data scientists prepare data, develop experiments, and deploy models at cloud scale.
The tutorials that follow this quickstart depend on this data to build a model that predicts the type of iris based on some of its physical characteristics.
The CLI interface allows you to access and interact with your Azure Machine Learning services using the az commands to perform all tasks required for an end-to-end data science workflow.
az ml --help If you're not going to continue to use this app, delete all resources created by this quickstart with the following steps so you don't incur any charges: You have now created the necessary Azure Machine Learning accounts and installed the Azure Machine Learning Workbench application.
Tutorial 2: Classify Iris - Build a model
Azure Machine Learning services (preview) are an integrated, data science and advanced analytics solution for professional data scientists to prepare data, develop experiments, and deploy models at cloud scale.
If you're not going to continue to use this app, delete all resources created by this quickstart with the following steps so you don't incur any charges: In this second part of the three-part tutorial series, you learned how to: Now, you can try out the third part of this tutorial series in which you can deploy the logistic regression model you created as a real-time web service.
Deploying externally generated Python/R Models as Web Services using Azure Machine Learning Studio
By Theo van Kraay, Data and AI Solution Architect at Microsoft Azure Machine Learning Studio is Microsoft’s graphical tool for Data Science, which allows for deploying externally generated machine learning models as web services.
This product was designed to make Data Science more accessible for a wider group of potential users who may not necessarily be coming from a Data Science background, by providing easy to use modules and a drag and drop experience for various Machine Learning related tasks.
Go here for a full end-to-end tutorial on how to prepare (part 1), build (part 2), and deploy/operationalise your models as web services using Docker (part 3) with Azure Machine Learning Workbench.
The purpose of this article is to take you through how to deploy an externally trained and serialised sklearn Python machine learning model, or a pre-saved model generated in R, as a web service using the Studio features.
First, we generate a simple model in Python using the pickle module, training the model using a .csv file that contains a sample from iris data set:
In the “search experiment items” box, search for each of the below, and drag each into the canvas: When they are on the canvas, connect iris_input.csv and model.zip to the “Execute Python Script” module as illustrated below:
Highlight the execute Python Script Module, and an Execute Python Script pane will appear, click the highlighted icon below to expand it so you can edit the code (note: you will need to ensure that the Python version selected contains a version of the pickle module that matches the one used to originally create the serialized model) :
Although performance may be adequate for small models and limited throughout, since the Azure Machine Learning Environment is a managed service (where you are not in control of the physical resources) and the model is being de-serialized at runtime for each execution, you may need to consider the performance characteristics.
Tutorial 3: Classify Iris: Deploy a model
Azure Machine Learning (preview) is an integrated, end-to-end data science and advanced analytics solution for professional data scientists.
The scoring script loads the model.pkl file from the current folder and uses it to produce new predictions.
As an alternative to the az ml service create realtime command shown previously, you also can perform the steps separately.
To test the irisapp web service that's running, use a JSON-encoded record containing an array of four random numbers.
If you're not going to continue to use this app, delete all resources created by this quickstart with the following steps so you don't incur any charges: In this third part of the three-part tutorial series, you have learned how to use Machine Learning to: You have successfully run a training script in various compute environments.
Build Deploy Predictive Web Apps Using RStudio and Azure ML
In this post, we will walk through the process of building a machine learning model in R, deploying it as a web service in Azure ML, and then creating and deploying a web application that does predictions in real time.
Get IRIS datasetlibrary(RCurl)iris_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"iris_txt = getURL(iris_url)iris_data = read.csv(textConnection(iris_txt), header = FALSE) #Rename the columnslibrary(plyr)names(iris_data)iris = rename(iris_data, c("V1"="Sepal_Length", "V2"="Sepal_Width","V3"="Petal_Length", "V4"="Petal_Width", "V5"="Species"))names(iris) #Fit the modellibrary(e1071)fit <- naiveBayes(Species~., data=iris)
#Summarize model accuracytable(predictions, iris$Species) #Do a prediction using the model and a row of datapredict <- predict(fit, c(5.1,3.5,1.4,0.2))predict We can now create the function that will do the prediction.
The function takes four input parameters (the features of the Iris dataset), and returns a prediction (the name of the Species) using the model called “fit”
Note that you will need to define the types of the input parameters (here all “float”), and the return parameter (called Species) with a type of int.
We will use this information in the next section to create and deploy a web application that can call the API for real-time predictions.
Using the web service URL and API Key, you can construct a JSON request that can call the service to make predictions in real time or in batch mode.
The template uses the web service’s API URL and Key from the response to the PublishWebService function call above to auto-generate and deploy an Azure web application so there is no code to write.
In this post, we built a machine learning model in R, deployed it as a web service on Azure ML, and created and deployed a web application to Azure without writing any ASP.NET Code.
Azure ML Operationalization APIs let you deploy machine learning models as web services from familiar authoring environments such as RStudio and Jupyter (formerly iPython) making them accessible over the internet at a high scale.
Tutorial 1: Classify Iris - Preparing the data
Azure Machine Learning service (preview) is an integrated, end-to-end data science and advanced analytics solution for professional data scientists to prepare data, develop experiments, and deploy models at cloud scale.
If you're not going to continue to use this app, delete all resources created by this quickstart with the following steps so you don't incur any charges: In this tutorial, you used Azure Machine Learning Workbench to: You are ready to move on to the next part in the tutorial series, where you learn how to build an Azure Machine Learning model:
- On Tuesday, January 22, 2019
Operationalize your models with Azure Machine Learning - BRK2290
This is the introduction to the new Azure ML features.
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