{"id":1320,"date":"2019-05-09T00:00:00","date_gmt":"2019-05-09T07:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/blog\/simplifying-ai-with-automated-ml-no-code-web-interface"},"modified":"2025-06-23T23:44:38","modified_gmt":"2025-06-24T06:44:38","slug":"simplifying-ai-with-automated-ml-no-code-web-interface","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/simplifying-ai-with-automated-ml-no-code-web-interface\/","title":{"rendered":"Simplifying AI with the new automated machine learning UI"},"content":{"rendered":"\n
\"Leverage<\/figure>\n\n\n\n

Leverage the power of automated machine learning<\/em><\/p>\n\n\n\n

Artificial Intelligence (AI) has become the hottest topic in tech. Executives and business managers, analysts and engineers, developers, and data scientists, all want to leverage the power of AI to gain better insights to their work and better predictions for accomplishing their goals.<\/p>\n\n\n\n

While businesses are beginning to fully realize the potential of machine learning (ML), it requires advanced data science skills that are hard to come by. There are many business domain experts who have a general understanding of machine learning and predictive analytics; however, they prefer not to dwell into the depths of statistics or coding which are required when working with traditional ML tools.<\/p>\n\n\n\n

With the launch of automated machine learning<\/a> in Azure Machine Learning service<\/a> last December, we have started the journey to both accelerate<\/strong> and simplify <\/strong>AI. This helps data scientists, who want to automate part of their ML workflow so they can spend more time focusing on other business objectives. It also makes AI available for a wider audience of business users who don\u2019t have advanced data science and coding knowledge. One recent example is the integration with Power BI<\/a>, which enables the accessibility of ML to data analysts.<\/p>\n\n\n\n

We are excited to announce a new automated machine learning web user interface (UI) in Azure portal, available now in preview.<\/strong><\/p>\n\n\n\n

\"AutoML<\/figure>\n\n\n\n

Emphasizing our mission to scale machine learning to the masses, we now introduce automated machine learning user interface (UI), which enables business domain experts to train ML models without requiring expertise in coding. Users can import their own data and, within a few clicks, start training on it. Automated machine learning will try a plethora of different combinations of algorithms and their hyperparameters to come up with the best possible ML model, customized to the user\u2019s data. They can then continue and deploy the model to Azure Machine Learning service as a web service, to generate future predictions on new data.<\/p>\n\n\n\n

Whether you\u2019d like to predict customer churn, detect fraudulent transactions, or forecast demand, the most important knowledge you\u2019ll need is to understand your data. Automated machine learning will find the best model for you and help you understand how well it will perform when making predictions on new data.<\/p>\n\n\n\n

To start exploring the automated machine learning UI, simply go to Azure portal<\/a> and navigate to an Azure machine learning workspace, where you will see \u201cAutomated machine learning\u201d under the \u201cAuthoring\u201d section. (If you don\u2019t have an Azure machine learning workspace yet you can learn how to create a workspace here<\/a>.)<\/p>\n\n\n\n

\"Authoring<\/figure>\n\n\n\n

Building models made easier<\/h2>\n\n\n\n

Let\u2019s take a look at how easy it is to build and train models with the new user interface.<\/p>\n\n\n\n

Quickly setup a new experiment<\/h3>\n\n\n\n

Starting the experiment is fast and easy. First, select a name for the experiment. After, you can choose the compute type to use for data exploration and training. For users who do not have a compute, you will find it easy to create one from this page.<\/p>\n\n\n\n

\"Creating<\/figure>\n\n\n\n

Review and explore data<\/h3>\n\n\n\n