Azure Machine Learning Studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.ML Studio is really great and even though you still need your statistic knowledge you can build, test and even deploy a machine learning model without writing a single line of code. It allows for this by offering prebuilt building blocks that can be customized and connected together using a visual interface. To get started, you first need to navigate to Azure ML Studio and sign in with a Microsoft Account. Once registered and signed in, you will see the homepage which provides you with multiple tabs. To create a new experiment you need to navigate to the experiment tab and click on the New button.

In this AI workshop, you are going to build a model to predict the bike demand for a specific hour of a day for the city of Washington. The data is available as sample data in the Azure ML Studio (classic) and is based on the data that has been collected in 2011 and 2012 in Washington. The dataset contains whether information and the number of bikes that have been rented. More information can be found on the UCI Machine Learning repository site: https://archive.ics.uci.edu/ml/datasets/bike We highly recommend you visit that site and investigate what kind of data you have available. Note: this workshop is to get in touch with machine learning.

If you've never used Azure Machine Learning Studio before, this tutorial is for you. In this tutorial, we'll walk through how to use Studio for the first time to create a machine learning experiment. The experiment will test an analytical model that predicts the price of an automobile based on different variables such as make and technical specifications. This tutorial shows you the basics of how to drag-and-drop modules onto your experiment, connect them together, run the experiment, and look at the results. We're not going to discuss the general topic of machine learning or how to select and use the 100 built-in algorithms and data manipulation modules included in Studio.

If you've never used Azure Machine Learning Studio before, this tutorial is for you. In this tutorial, we'll walk through how to use Studio for the first time to create a machine learning experiment. The experiment will test an analytical model that predicts the price of an automobile based on different variables such as make and technical specifications. This tutorial shows you the basics of how to drag-and-drop modules onto your experiment, connect them together, run the experiment, and look at the results. We're not going to discuss the general topic of machine learning or how to select and use the 100 built-in algorithms and data manipulation modules included in Studio.

Here is where we take advantage of AzureMl's newest feature: the Create R Model module. Now we can use R's randomForest library and take advantage of its large number of adjustable parameters directly inside AzureML studio. Then, the model can be deployed in a web service. Previously, R models were nearly impossible to deploy to the web. For a detailed explanation of setting up data partitions and model training checkout our other tutorial here.