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Build a news recommender application with Amazon Personalize

AWS Machine Learning

A Lambda function performs the same data transformation operations as the batch ingestion job at the individual record level, and ingests the data into Amazon Personalize using the PutEvents and PutItems APIs. For more information about these metrics, see Evaluating a solution version with metrics.

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Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

When the user is authenticated, the web application establishes a secure GraphQL connection to the AWS AppSync API, and subscribes to receive real-time events such as new calls and call status changes for the meetings list page, and new or updated transcription segments and computed analytics for the meeting details page.

APIs 107
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Promote feature discovery and reuse across your organization using Amazon SageMaker Feature Store and its feature-level metadata capability

AWS Machine Learning

For example, the information can include a description of the feature, the date it was last modified, its original data source, certain metrics, or the level of sensitivity. In this section, we interact with the Boto3 API endpoints to update and search feature metadata. 1]) df=df.drop('FeatureGroupArn', axis=1) return df.

APIs 81
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How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

AWS Machine Learning

The platform has shown a 3% boost to customer engagement metrics tracked (liking a show, following a creator, enabling upcoming show notifications) since its launch in May 2022. The independent nature of each individual metric transformation also makes Lambda, which is a stateless service on its own, a good fit for this problem.

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Fine-tune your Amazon Titan Image Generator G1 model using Amazon Bedrock model customization

AWS Machine Learning

Text-to-image models also enhance your customer experience by allowing for personalized advertising as well as interactive and immersive visual chatbots in media and entertainment use cases. The data remains in the same Region where the API call is processed. This process can be done both via the Amazon Bedrock console or APIs.

APIs 84
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Foundational vision models and visual prompt engineering for autonomous driving applications

AWS Machine Learning

Although this post focuses on autonomous driving, the concepts discussed are applicable broadly to domains that have rich vision-based applications such as healthcare and life sciences, and media and entertainment. For example, we can use an API chain to call an API and invoke an LLM to answer the question based on the API response.

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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Evaluate model performance on the hold-out test data with various evaluation metrics. This notebook demonstrates how to use the JumpStart API for text classification. frames ) profound ethical and philosophical questions in the form of dazzling pop entertainment". Fine-tune the pre-trained model on a new custom dataset.

Scripts 71