Automated Machine Learning with Power BI
Automated Machine Learning (AutoML) is transforming the analytics space by allowing non-Machine Learning experts to develop and build ML models. With AutoML implemented into business intelligence tools you already use, like Power BI, users are given a more integrated ML experience.
These days, advanced analytic techniques are becoming more and more common when creating business solutions. Two fields in particular, machine learning and artificial intelligence, have become prominent in solving issues such as: predictive sales, fraud detection, transportation optimization, and countless others.
Now, we are able to take advantage of these advanced capabilities without having any prior machine learning experience. Automated Machine Learning, also known as AutoML, is a technology that strives to achieve widespread machine learning availability. This tool provides the powerful, problem-solving features of machine learning and artificial intelligence without requiring years of experience from a data scientist.
Usability
AutoML is integrated into both Microsoft Azure and Power BI, which enables us to use these advanced techniques in software we are already familiar with. While using AutoML in Azure provides greater flexibility to end-users, it generally appeals to a more niche subset of overall users. This is because it requires a relatively experienced user with credentialed access to a machine learning-specific environment.
However, the Power BI AutoML integration is a lot more accessible to new users. Its no-code environment enables users to quickly build and train models as well as create dashboards off of them within their Power BI workspace.
Users can select the model that works best for their use case
Access to Data
Automated Machine Learning with Power BI presents users with the capability to build models using your Power BI datasets, alongside data from other sources. Since all the data within Power BI is already structured and designated in a certified workspace, it requires little effort for the user to begin modeling.
Once models have been built, the results can be fed back into Power BI datasets and data flows, which can be certified by data curators. This integration with Power BI not only makes it easier to develop models, but it also makes the results from these advanced techniques easy to distribute. Others can connect to new data marts which contain insights from machine learning without ever having to learn AutoML.
Users can apply results from the model into existing Power BI datasets
When to Use
Since this integration of AutoML has been designed to work with Power BI, there are some feature limitations, and it’s important to understand how to use Power BI AutoML to get the most value. AutoML in Power BI excels at solving well-defined scenarios rather than exploratory data and unsupervised analytics. It’s a great way to get started with building models and advanced analytics.
Computing
Another variable to understand is the computational resources required. The main significance here is that Power BI handles these resources automatically, unlike AutoML in Azure which leaves the option up to the end-user.
A more sophisticated and customized model may need greater computational power, but for a basic model, this computing is more than enough.
Conclusion
These are just a few of the topics and considerations involved with using AutoML in your Power BI environment. While this introduction to AutoML’s capability may be brief, we hope this helps you begin your journey to using advanced analytics in everyday business solutions.
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