Businesses of all sizes are trying to meet their users expectations of personalized recommendations in online shopping, online streaming content, and their digital media libraries of books, articles, music and podcasts. In this session we’ll share how customers have leveraged the personalization experience from Amazon.com, to offer product recommendations, similar item recommendations, and create re-ranking within each user experience. With no prior machine learning experience you can start creating A/B tests to see the impact of Amazon Personalize on increasing user engagement with your recommended products and content. Speaker: Divyesh Sah, AWS Solutions Architect
This session walks technology leaders through the largest Canadian influences to AI/ML including the latest...
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For businesses working with AI and ML however, living this never normal is simply ‘business as usual’, where constant change offers abundant opportunities to innovate, and thrive.
In this session, we share specific examples from Amazon.com's consumer/retail and other businesses to explain how AI/ML helps Amazon deliver the best customer experience possible.
Learn how executives and managers who are looking to achieve success using ML to accelerate innovation and drive technological progress.
This session walks technology leaders through the largest Canadian influences to AI/ML including the latest Canadian investment by AWS in this space.
Discover how using Amazon Textract, Amazon Comprehend, and Amazon Augmented AI provide organizations with a machine learning solutio to overcome document processing and analysis at scale.
Here the latest security updates in the Well-Architected categories of detection, identity management, data protection, and incident response.
This session will provide a high level overview of cognitive search, why it's important, and what it can do for your customers and employees.
Take a deep dive into Amazon Fraud Detector, its use cases in your business, and how to quickly get started.
In this whiteboarding session, learn how to design an ML application guided by the AWS Well-Architected Framework five pillars.
Discover ways to use Apache Spark on AWS to analyze large datasets, perform data quality checks, transform raw data into machine learning features, and train predictive models.
In this session, you'll see how to create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines.
Using the popular Hugging Face Transformers open source library for BERT to train and deploy multiple natural language understanding (NLU) models.
In this session, we walk through how to use the real-time training metrics and set up alerts so you can reduce troubleshooting time, training costs and improve model quality.
Learn how to solve all these problems with Amazon SageMaker Feature Store, and how to use it with both the SageMaker Studio user interface and the SageMaker SDK.
In this session, explore how to choose the proper instance for ML training and inference based on model size, complexity, throughput, framework choice, inference latency and portability requirements.