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

AWS Machine Learning

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. Text classification.

Scripts 71
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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. Lambda enabled the team to create lightweight functions to run API calls and perform data transformations. For more information, you can also check out other customer use cases in the AWS Analytics Blog.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

Consider inserting AWS Web Application Firewall (AWS WAF) in front to protect web applications and APIs from malicious bots , SQL injection attacks, cross-site scripting (XSS), and account takeovers with Fraud Control. He recharges through reading, traveling, food and wine, discovering new music, and advising early-stage startups.

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Power recommendations and search using an IMDb knowledge graph – Part 3

AWS Machine Learning

Many AWS media and entertainment customers license IMDb data through AWS Data Exchange to improve content discovery and increase customer engagement and retention. In this post, we illustrate how to handle OOC by utilizing the power of the IMDb dataset (the premier source of global entertainment metadata) and knowledge graphs.

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Upscale images with Stable Diffusion in Amazon SageMaker JumpStart

AWS Machine Learning

In this post, we provide an overview of how to deploy and run inference with the Stable Diffusion upscaler model in two ways: via JumpStart’s user interface (UI) in Amazon SageMaker Studio , and programmatically through JumpStart APIs available in the SageMaker Python SDK.

APIs 72
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How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. The ML components for data ingestion, preprocessing, and model training were available as disjointed Python scripts and notebooks, which required a lot of manual heavy lifting on the part of engineers.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

AWS Machine Learning

Option B: Use a SageMaker Processing job with Spark In this option, we use a SageMaker Processing job with a Spark script to load the original dataset from Amazon Redshift, perform feature engineering, and ingest the data into SageMaker Feature Store. The environment preparation process may take some time to complete.

APIs 73