From the course: Delivering Data-Driven Decisions with AWS: Applying Machine Learning, Data Engineering, and Generative AI
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Introduction to Amazon SageMaker Canvas
From the course: Delivering Data-Driven Decisions with AWS: Applying Machine Learning, Data Engineering, and Generative AI
Introduction to Amazon SageMaker Canvas
- [Instructor] Imagine we work in a busy contact center and we are tasked with predicting call volumes and generating automated email responses. Let's build together with Amazon's SageMaker Canvas. In this video, we will learn about no-code machine learning and generative AI. Amazon's SageMaker Canvas empowers business analysts to make decisions with machine learning and artificial intelligence predictions. No coding or machine learning experience is required. We may bring in our own data and use pre-trained custom models. Some of the features include a visual interface, importing existing machine learning models, join data sets, publish models, and share models with data scientists. With ready-to-use models, we can access sentiment analysis and foundation models to enable generative AI. We may also create custom models for prediction. Ready-to-use models are powered by AWS AI services. With our use case, we can check if…
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