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Periscope Data and Amazon Web Services Make Machine Learning in Minutes a Reality

New Private Beta Leveraging Amazon SageMaker Available Today for Data Pros to Prepare, Build and Collaborate on Machine Learning Projects

Periscope Data, the advanced analytics platform that brings the power of data science to Business Intelligence (BI), today announced a machine learning (ML) solution that leverages Amazon SageMaker as part of a comprehensive offering for data teams looking to solve complex ML problems within one seamless workflow. This solution is available for customers today to test as part of a private beta program. Rover, the world’s largest network of pet sitters and dog walkers, is among the first to join the program and will be showcasing their enhanced ML workflow at an event on July 18, following this week’s Amazon Web Services (AWS) Summit in New York.

Today, fragmented workflows for data analysts building ML models involve many manual steps and disparate tools. Data preparation and feature engineering are particularly difficult because of the iterative experimentation involved in the ML process. These issues create challenges when accessing and combining data sources and discourage collaboration and reporting on ML insights. With businesses of all sizes making substantial investments in ML while developing algorithms to address their most complex data problems, these issues are widespread and pervasive.

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To address these issues, Periscope Data’s new functionality with Amazon SageMaker will allow data analysts to tackle ML problems in one seamless workflow, from data prep to model training to reporting.

First, Periscope Data simplifies data prep, by surfacing distribution and descriptive statistics, helping users recognize where more data cleaning or normalization is required. Data teams can also rapidly iterate on feature selection and develop even the most complex feature engineering transformations by leveraging SQL in views for dataset development within Periscope Data. Next, the datasets are directly and effortlessly exported into Amazon SageMaker to build, train and deploy ML models at scale. Model results are then written back to a data warehouse, allowing users to leverage trained models for predictive and prescriptive reporting in Periscope Data to inform business and product decisions.

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“Building trustworthy machine learning algorithms is a huge challenge for today’s data teams, in part because of the difficulties they face with data prep and feature engineering,” said Harry Glaser, co-founder and CEO, Periscope Data. “As we continue to fulfill our mission of turning data teams into superheroes, we see this integration with Amazon SageMaker as a great way to simplify machine learning workflows for our customers and give them a faster path to valuable insights.”

Amazon SageMaker is a fully managed service used by companies worldwide to empower developers with easier ways to leverage ML. Amazon SageMaker allows developers and data scientists to quickly and easily build, train and deploy ML models at any scale.

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Swami Sivasubramanian
Swami Sivasubramanian

“Amazon SageMaker gives life to machine learning projects by speeding up the process for everyday developers,” said Swami Sivasubramanian, vice president of machine learning, Amazon Web Services Inc. “In order to do that, it’s critically important that we work with the industry leaders in data analytics solutions to accelerate the impact of machine learning. We’re very excited to have Periscope Data as one of our first direct integrations with Amazon SageMaker, and we’re looking forward to seeing what our customers will create with this new functionality.”

AWS and Periscope Data will be hosting a joint event on Wednesday, July 18, directly following AWS Summit New York2018. This half-day training session will include an overview of the functionality available in the beta program, a presentation from the data team at Rover, an alpha user of the integration, and interactive walkthroughs of Amazon SageMaker to provide additional context on its capabilities for completing the ML lifecycle.

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By using the technology created by Periscope Data and AWS, Rover is now feeding their data into a customized ML model, allowing them to more intelligently pair pet owners with the right services to ensure higher value and to accurately forecast customer retention and inform product enhancements. 

“Answering more complex questions with predictive modeling and machine learning is a core part of our data strategy as we continue to grow,” said Brandyn Lee, senior data scientist at Rover. “Periscope Data has been an ideal analytics platform for us to get insights on our data quickly, and we’re excited about digging in deeper with this integration to build out our machine learning efforts.”

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Earlier this year, Periscope Data announced it had achieved Advanced Technology Partner status in the AWS Partner Network (APN) for recognition of its overall business investment on AWS. Periscope Data builds on its analytics and data ingest tools with a built-in, fully managed data warehouse based on Amazon Redshift. This allows data teams to connect to their full range of business data and easily store and manage it for insightful queries and analysis. Periscope Data is also supporting Amazon Redshift Spectrum and other innovations on AWS in its platform.

The Periscope Data integration with Amazon SageMaker will be made available publicly in the coming months. To attend the July 18 event, sign up here, and to learn more about joining the beta program please request access here or contact a Periscope Data representative for details.

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