Jumpstart to prepare, build, train, and deploy ML models on Amazon Sagemaker

Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. In this session, we provide an overview for one of the fastest growing services in AWS history. Amazon SageMaker is built on Amazon’s two decades of experience developing real-world machine learning applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices. Learn how to prepare, build, train, tune, deploy, and manage your first machine learning model on AWS.

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

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.

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