{"id":14143,"date":"2018-02-16T19:20:18","date_gmt":"2018-02-17T00:20:18","guid":{"rendered":"https:\/\/www.sherweb.com\/blog\/?p=14143"},"modified":"2023-05-15T11:06:08","modified_gmt":"2023-05-15T15:06:08","slug":"benefits-machine-learning-microsoft-azure","status":"publish","type":"post","link":"https:\/\/www.sherweb.com\/blog\/cloud-server\/benefits-machine-learning-microsoft-azure\/","title":{"rendered":"3 machine learning benefits for Microsoft Azure"},"content":{"rendered":"

What is machine learning? Let\u2019s \u00a0look at an example. In this world, we have humans and machines. The difference is that humans learn from past experiences whereas machines need regular instructions to perform specific tasks.<\/p>\n

But the question is, can we get computers to learn from past experiences too? And, the answer is: yes we can! When a machine learns from past experiences, this process is called \u201cMachine Learning.\u201d<\/p>\n

This is part of artificial intelligence. It focuses on the development of computer programs that retrieves data and uses it to learn and process new information.<\/p>\n

Although numerous small and big machine learning service providers have emerged in the market; Azure Machine Learning Studio remains a reliable name, offering advanced features.<\/p>\n

Azure Machine Learning Studio<\/h2>\n

After the great success of Power BI and Azure HDInsight, Microsoft developed a new product \u2013 \u201cAzure Machine Learning Studio.\u201d<\/p>\n

Launched in June 2014, Azure ML takes predictive analytics to a public cloud. It operates on the Azure public cloud and users don\u2019t need to purchase any software and hardware separately. They also no longer need to worry about maintenance and deployment. Azure ML is a simple drag-and-drop platform, which can build, test, and place predictive analytics solutions on your data.<\/p>\n

The tool has the potential to solve large data processing projects, and for that reason, it is recognized today as a game changer in the data modeling world. ML Studio allows non-data science users to build data models easily using data flow diagrams and drag-and-drop gestures. The platform not only minimizes the coding requirement but also saves time through Machine Learning Studio\u2019s library of sample experiments.<\/p>\n

How it works<\/h2>\n

Using and setting up the Machine Learning workspace is simple and straightforward. You can log into the web service accessing: https:\/\/studio.azureml.net\/. Each time you need to create something, you just click on the \u201c+ New\u201d button on the bottom left window of your database.<\/p>\n

To create a predictive analysis model, we usually pull data from different sources. This data is analyzed, calculated, and transformed through a combination of diverse statistical functions and data manipulation before producing final results.<\/p>\n

Creating a predictive analysis model is a repetitive process as you need to modify various functions and their parameters many times until you can find a trained predictive analysis model. Azure ML is the platform where cloud resources, predictive analytics, and your data hook up with each other.<\/p>\n

The entire platform works with machine learning algorithms that learn from previous inputs and predict data accurately, every time. The advantage of Azure Machine Learning Studio is there is no need for coding and programming to create learning algorithms. You just drag-and-drop your datasets and analysis modules and link them together to create an experiment that runs in Machine Learning Studio.<\/p>\n

To practice on your model design, edit your experiment and save a copy and rerun it. This process happens many times. When your training experiment is over, you can convert it to a predictive experiment. Now your experiment is ready to publish as a web service that is accessible by you and future users. The platform offers a shared visual workspace for testing and building a predictive analysis model.<\/p>\n

Benefits of Azure Machine Learning<\/h2>\n