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Using data in your data warehouse for machine learning use cases like churn prediction can be complicated because of the different tools and skills required. In this session, learn how with Amazon Redshift Machine Learning, you can use SQL to automatically create, train, and apply machine learning (ML) models with the data in your data warehouse using familiar SQL commands. Join this session to learn how to leverage this new Amazon SageMaker integration to embed predictions like fraud detection and risk scoring directly in queries and reports, without any prior ML experience.

Learning Objectives:
– Learn the fundamentals of building, training & deploying machine learning models
– Learn how Amazon SageMaker provides managed distributed training for machine learning models with a modular architecture
– Learn to quickly and easily build, train & deploy machine learning models using Amazon SageMaker and Redshift

Learn more: https://aws.amazon.com/sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this tech talk, we will introduce you to the concepts of Amazon SageMaker including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment of ML models. With zero setup required, Amazon SageMaker significantly decreases your training time and the overall cost of getting ML models from concept to production.

Amazon Redshift is a data warehouse product which forms part of the larger cloud-computing platform Amazon Web Services. The name means to shift away from Oracle, red being an allusion to Oracle, whose corporate color is red and is informally referred to as “Big Red”.


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