Machine learning (ML) has become a key technology ingredient for businesses across industries to deliver delightful customer experiences. However, the proliferation of data science tools that are difficult to integrate can create barriers to innovation and reduce data scientist productivity. Amazon SageMaker brings together purpose-built tools for every step of the ML lifecycle under the SageMaker Studio integrated development environment, making it easy to build, train, and deploy ML models. In this tech talk, learn how to easily prepare data and build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows using Amazon SageMaker.
Learning Objectives:
* Objective 1 - Discover how to use the end-to-end SageMaker capabilities for every step of the ML lifecycle.
* Objective 2 - Learn how to get started quickly using SageMaker JumpStart pre-built solutions and SageMaker Autopilot.
* Objective 3 - Find out about tutorials and technical resources to get hands-on experience and dive deeper.
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