Microsoft announces AI model builder, key driver analysis, and Azure Machine Learning integration for Power BI

• Updated on: 2018-11-22
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Microsoft’s Power BI, a business analytics service that enables users to create reports, dashboards, and more without coding experience or deep technical expertise, is getting a dose of hefty artificial intelligence (AI). The Seattle company announced today several new AI features available in preview — including image recognition and text analytics, key driver analysis, machine learning model creation, and Azure Machine Learning integration — designed to “surface the work of data scientists” and “empower more users to leverage AI.”

“I think the ability to tap into a much broader audience, both with the cognitive services lighting up and Power BI — that’s cool,” Eric Boyd, corporate vice president of AI Platform at Microsoft, told VentureBeat in an interview. “The automated machine learning lighting up in Power BI is really cool because now, you don’t have to be a data scientist to build and train a model.”

Azure Cognitive Services — Microsoft’s suite of machine learning APIs and pretrained AI models can identify patterns in datapower BI’s image recognition and text analytics features. In Power BI, they drive object recognition and detection, sentiment analysis, and phrase detection, all of which can be applied to data sources like documents, images, social media feeds, and more.

A hotel chain could use Azure Cognitive Services in Power BI to analyze reviews and pinpoint problem areas, for example. Or a brick-and-mortar retailer could tap it for automated inventory management.

“Azure Cognitive Services capabilities in Power BI can surface [these] insight’s automatically,” Arun Ulagaratchagan, general manager of Power BI, wrote in a blog post. “This enables [them to] discover hidden, actionable insights in their data and drive better business outcomes with easy-to-use AI.”

Meanwhile, Power BI’s new key driver analysis feature helps track metrics and performance indicators — specifically by analyzing data, ranking important items, and surfacing them. (Think business KPIs.) And the AI model builder — a simplified, no-code version of Azure Machine Learning’s automated machine learning tools that’s been reworked for “common use cases” — trains, tests, and validates systems in “just a few clicks.” It even selects the best algorithm automatically.

“Understanding which model to use is probably [going to get] a lot of people past major hurdle,” Boyd said. “What we basically do is we have an AI model that predicts which AI model is going to perform best and iterates and learns. That’s a little bit meta, but it’s really powerful … I think that’s going to open the door to a lot of people.”

Complimenting those features, Power BI now plays nicely with Azure Machine Learning. AI models built in Azure can be shared within Power BI; Power BI autonomously discovers models to which each user has access and automatically creates a point-and-click user interface to invoke them.

“Advanced machine learning requires specialized data science tools. Azure Machine Learning is a platform where data scientists develop machine learning models to take on complex business challenges,” Ulagaratchagan wrote. “This makes collaboration among business analysts and data scientists easier and faster than ever before. Complex tasks that typically require technical know-how … will now be possible with just a few clicks and without code.”

The new features join Power BI’s other AI-centric capabilities, such as its natural language question and answer framework and Quick Insights, which derives insights from large datasets. And they follow hot on the heels of previous Power BI enhancements — perhaps most notably Power BI Insights, a collection of apps with prebuilt intelligent dashboards informed by customers’ data.

In September, Microsoft announced a number of new AI feature for the workplace, some of which dovetail with the Power BI capabilities announced this week. It made Speech Service — its speech recognition and translation solution — generally available, and introduced AI models compatible with field programmable gate array (FPGA) chips that can accelerate AI training. It also introduced a Python software development kit (SDK) for Azure Machine Learning, the Cortana Skills Kit for Enterprise, AI-powered time-encoded meeting transcriptions and background blur features in Microsoft Teams, and enhanced search and Excel tools in Microsoft 365.

“Our goal is to make AI accessible and valuable to every individual and organization, amplifying human ingenuity with intelligent technology,” Lili Cheng, corporate vice president of Conversational AI at Microsoft, wrote in a blog post today. “To do this, Microsoft is infusing intelligence across all its products and services to extend individuals’ and organizations’ capabilities and make them more productive, providing a powerful platform of AI services and tools that makes innovation by developers and partners faster and more accessible, and helping transform business by enabling breakthroughs to current approaches and entirely new scenarios that leverage the power of intelligent technology.”