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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

AWS Machine Learning

However, traditional dubbing methods are costly ( about $20 per minute with human review effort ) and time consuming, making them a common challenge for companies in the Media & Entertainment (M&E) industry. Yaoqi Zhang is a Senior Big Data Engineer at Mission Cloud.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 135
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. We will start by using the SageMaker Studio UI and then by using APIs.

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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table.

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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

It stores history of ML features in the offline store (Amazon S3) and also provides APIs to an online store to allow low-latency reads of most recent features. With purpose-built services, the Amp team was able to release the personalized show recommendation API as described in this post to production in under 3 months. Conclusion.

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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

When the message is received by the SQS queue, it triggers the AWS Lambda function to make an API call to the Amp catalog service. The Lambda function retrieves the desired show metadata, filters the metadata, and then sends the output metadata to Amazon Kinesis Data Streams. Data Engineer for Amp on Amazon.

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Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

AWS Machine Learning

This example notebook demonstrates the pattern of using Feature Store as a central repository from which data scientists can extract training datasets. In addition to creating a training dataset, we use the PutRecord API to put the 1-week feature aggregations into the online feature store nightly. Nov-01,22:01:00 1 74.99 …9843 99.50