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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.

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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. Conclusion.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

Fine-tuning the model on custom fashion data such as fashion images along with text describing the attributes of fashion products can be used to generate metadata that then improves a user’s search experience. Marketing – Another use case of image search is digital asset management.

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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

Feature store ML models rely on well-engineered features coming from a variety of data sources, with transformations as simple as calculations or as complicated as a multi-step pipeline that takes hours of compute time and complex coding. Creating the Apache Flink application using Flink’s SQL API is straightforward.

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Why Behavioural Data and Behavioural Analytics Have Become the Secret Ingredient for Product Led Growth!

SmartKarrot

When companies discovered this and User Interface (UI) and Customer Experience (CX) overtook other measures of SaaS success, the way business is done shifted from market oriented one to a product led one. What is the one biggest worry of SaaS businesses and their marketing teams? Market demands and product change requirements?