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Predictive analytics can provide your organization with data insights and differentiation to rise above the competition. However, Machine learning (ML) outcomes are only as good as the data they are built upon. Getting the data ready for accurate modeling is time consuming, cumbersome, and a waste of data professionals’ skills to be polishing the materials they rely on while they should focus on the work that matters—creating accurate predictions that improve products, services, and organizational efficiency.

In this latest webinar, we will see how the data preparation process can be streamlined to produce an accurate model for Amazon SageMaker. Guest speaker Kris Skrinak, Machine Learning Segment Lead from Amazon Web Services Partner Network will provide deep insights.

Vijay Balasubramaniam, Sr. Partner Solutions Architect – Trifacta
Kris Skrinak, Machine Learning Segment Lead – Amazon Web Services

""Trifacta brought an entirely new level of productivity to the way our analyst and IT teams explore diverse data and define analytic requirements. Our users can intuitively and collaboratively prepare the growing variety of data that makes up PepsiCo’s analytic initiatives.""

""We were actually able to shave the amount of time it took to do the analysis by [a factor of] six. Rather than having to do a tremendous amount of analysis, we’re actually readily able to start getting incremental data products out quickly.""