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 Data Science Central 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.

Join this webinar and you will learn:

• The modern scalable and agile cloud data pipeline for analytics and ML applications
• What it takes to deliver accurate models leveraging Amazon SageMaker
• Typical data flaws and how to remediate them with Trifacta data preparation solutions
• End to end demo from data acquisition, cleansing to feature engineering and modeling with Trifacta & Amazon SageMaker

Speakers:
Vijay Balasubramaniam, Sr. Partner Solutions Architect - Trifacta

Kris Skrinak, Machine Learning Segment Lead – Amazon Web Services

Hosted by:
Bill Vorhies, Editorial Director - Data Science Central