How to Wrangle Data for Machine Learning on AWS

Machine learning outcomes are only as good as the data they are built upon, but preparing data for machine learning is a time-consuming process. The work of preparing data (data wrangling) for analytics can consume over 80% of the project effort.

Data wrangling solutions running on Amazon Web Services (AWS) can help streamline machine learning applications so that your teams can focus on the work that really matters: creating accurate predictions that improve your products, services, and your organization’s efficiency .

Listen to our webinar recording to hear how Consensus, a Target-owned subsidiary, utilizes AWS and Trifacta to prepare data for use in fraud detection algorithms. You’ll learn how self-service automated data wrangling can save your organization time and money, and tips for getting started with Trifacta’s solution, built for AWS.

Viewers will learn:

  • Why automating your data wrangling tasks can lead to greater data accuracy and more meaningful insights.
  • How you can reduce your data preparation time by 60% and more with self-service data wrangling tools built for AWS.
  • How easy it is to get started with machine learning solutions for data wrangling on the cloud.

Who Should Listen:

Analytics leaders (VPs/Directors/Heads of Analytics, Data Strategy, Customer Insights, Consumer Insights, Big Data, and/or Digital Strategy), Enterprise Information Managers or Directors, data scientists, data analysts, and all data-driven professionals are encouraged to attend this webinar.

AWS Speaker: Pratap Ramamurthy, Partner Solutions Architect

Trifacta Speaker: David McNamara, Customer Success Manager

Customer Speaker: Harrison Lynch, Sr. Director of Product Development, Consensus Corporation