Prologika Newsletter Spring 2020

Do you know that according to Gartner, at least five of the top 10 technology trends for 2020 will involve predictive analytics? And the third on the list is “democratization” to deliver it to non-specialists. With the growing demand for predictive analytics, Automated Machine Learning (AutoML) aims to simplify and democratize predictive analytics so business users can create their own predictive models. The promise of AutoML is to bring predictive analytics to business users, just like Power BI democratizes data analytics, Power Apps democratizes app dev, and Power Query democratizes data shaping and transformation.

 

Comparing Options

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.

	Power BI AutoML	Azure AutoML
Licensing	Power BI Premium	Azure ML (Enterprise Edition recommended)
Container	Dataflow	Experiment
Power Query	Available	Not available
Supported data sources	Many	A few (local files, Azure SQL DB, ADLS, and a few more)
Model	Not Accessible (Power BI handles everything)	Accessible
Web service endpoint	Not available outside Power BI	Available for app integration
Scoring	Apply the model to entity	Various options (Notebooks, SDK, custom integration)

To me, the best solution would have been the combination of both technologies. 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). I expect this to change as the product evolves.

Azure ML Studio

I’ve done recently some work with the new version of 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 to spend enormous time testing different algorithms.