As a business user, the two most popular options for applying Automated Machine Learning for predictive analytics are Power BI and AzureML. Behind the scenes, Power BI AutoML uses the automated machine learning feature of AzureML but there are differences and I summarize below the most important ones.

To me, the best solution would have been the combination of both. I like Power Query for sourcing, shaping and transforming the data, but I also like the flexibility that AzureML brings. Unfortunately, you can’t mix and match. It appears that AzureML has decided to roll out their own data connectivity mechanism and as a result, it supports a limited number of data sources (for example, on-prem data sources are not accessible). Of course, this will probably change soon as the product evolves.

I’ve done recently some work with Azure ML Studio (https://ml.azure.com/), and I’m impressed. Microsoft has learned important lessons from the previous AzureML (now called “classic”) and greatly enhanced the product. If you’re looking for a SaaS ML toolset that targets both business users and data scientists, AzureML should be on the top of your list. Speaking of its AutoML feature, the main advantages that it brings for predictive analytics are:

• Determining the model type – classification, regression, and time series forecasting (the last one is not available yet in Power BI)
• Automatic featurization
• Selecting the best algorithm – For example, the screenshot below shows how AzureML has tested various algorithms and determined that VotingEnsemble performs the best.

Even if you’re a data scientist, the best algorithm selection feature alone justifies giving AutoML a try – if not for anything else but to select the best algorithm so that you don’t have spend enormous time testing different algorithms.