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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Scripts 127
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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning

The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK. SageMaker runs the legacy script inside a processing container. Step Functions is a serverless workflow service that can control SageMaker APIs directly through the use of the Amazon States Language.

Scripts 144
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Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

APIs 105
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How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps

AWS Machine Learning

Problem definition Traditionally, the recommendation service was mainly provided by identifying the relationship between products and providing products that were highly relevant to the product selected by the customer. Lambda receives the list of recommendations and provides them to the API gateway.

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How Games24x7 transformed their retraining MLOps pipelines with Amazon SageMaker

AWS Machine Learning

The code to invoke the pipeline script is available in the Studio notebooks, and we can change the hyperparameters and input/output when invoking the pipeline. This is quite different from our earlier method where we had all the parameters hard coded within the scripts and all the processes were inextricably linked. cpu-py39-ubuntu20.04-sagemaker",

Scripts 107
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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

AWS Machine Learning

For this we use AWS Step Functions , a serverless workflow service that provides us with API integrations to quickly orchestrate and visualize the steps in our workflow. Create a SageMaker pipeline definition to orchestrate model building. Use the scripts created in step one as part of the processing and training steps.

Scripts 121
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Take your intelligent search experience to the next level with Amazon Kendra hierarchical facets

AWS Machine Learning

If you just want to read about this feature without running it yourself, you can refer to the Python script facet-search-query.py Set up the infrastructure and run the Python script to query the Amazon Kendra index. In the navigation pane, choose Facet definition. For convenience, all the steps are included in one Python script.

APIs 97