AWS Startups Blog

Periscope Data Builds ML Solution With Amazon SageMaker

When it comes to transforming floods of raw data into clear, decisive business intelligence, Periscope Data remains on the cutting edge. To keep their commitment to their consumers, Periscope Data has developed a machine learning solution that leverages Amazon SageMaker as a core part of its machine learning workflow.

The solution allows data analysts to manage data prep and feature engineering in Periscope Data, then effortlessly import to Amazon SageMaker to build, train and deploy machine learning models at scale, before returning to Periscope Data for analysis and reporting. “Periscope Data is known as the fastest, most flexible, most powerful BI product, used by the most sophisticated customers and the most advanced data analysts and data scientists,” Periscope Co-Founder and CEO Harry Glaser says. “They demand the newest capabilities and expect to be able to use data to make every kind of decision.”

“The fact is, if you want to be the company that is the most advanced and powerful in its field, then you have to demonstrate your commitment to your customers and future customers,” says Glaser. “A big part of that commitment is adopting new technologies faster than anyone else.”

For Periscope Data’s head of project management, Scott Castle, it made sense for the company to build its solution based on SageMaker: “SageMaker is the way that you get access to massive computing without having to have all of the hand-holding and the cotton candy fluff around it.”

In business, machine learning has been a discipline since the 1960s, but only in the past few years has the technology become readily accessible. This means that even data-literate companies are always iterating on their technology stack to become more statistically savvy. “It’s a pretty different way of operating than even five or ten years ago,” Glaser says. “Technology is getting more scalable and more accessible, and more people are getting data enabled.”

As machine learning becomes integrated into modern companies—be they at a global or local scale—more and more decisions will be made by a program’s ability to quickly scrutinize the data and yield answers. “What we see is customers increasingly trying to push the envelope on how many different types of data they can blend together in the same place, and then ask useful questions about,” Castle says.“If you’re collecting all the data coming in from every source of information that a company has access to, then you’re able to view the data holistically and make more informed decisions to understand market dynamics.”

Building a workflow around SageMaker’s machine learning capabilities allows Periscope Data to continually provide such answers for their own customers and give them an edge. “If I can understand those better than anybody else, then I can make a business model that always wins,” Castle says. “I’ve basically got a clearer picture of the world.”

Michelle Kung

Michelle Kung

Michelle Kung currently works in startup content at AWS and was previously the head of content at Index Ventures. Prior to joining the corporate world, Michelle was a reporter and editor at The Wall Street Journal, the founding Business Editor at the Huffington Post, a correspondent for The Boston Globe, a columnist for Publisher’s Weekly and a writer at Entertainment Weekly.