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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

Run the script init-script.bash : chmod u+x init-script.bash./init-script.bash init-script.bash This script prompts you for the following: The Amazon Bedrock knowledge base ID to associate with your Google Chat app (refer to the prerequisites section). The script deploys the AWS CDK project in your account. Choose Save.

APIs 118
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

The following diagram depicts an architecture for centralizing model governance using AWS RAM for sharing models using a SageMaker Model Group , a core construct within SageMaker Model Registry where you register your model version. The MLE is notified to set up a model group for new model development.

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning

We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw. Compared to Llama 1, Llama 2 doubles context length from 2,000 to 4,000, and uses grouped-query attention (only for 70B). 4096 2 8 4 1 256 7.4. 4096 4 8 4 1 256 14.6.

Scripts 118
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Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning

You can then iterate on preprocessing, training, and evaluation scripts, as well as configuration choices. framework/createmodel/ – This directory contains a Python script that creates a SageMaker model object based on model artifacts from a SageMaker Pipelines training step. script is used by pipeline_service.py The model_unit.py

Scripts 112
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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

client("sagemaker") create_model_package_group_response = sm_client.create_model_package_group( ModelPackageGroupName=model_package_group_name, ModelPackageGroupDescription="Cross account model package group for mammo severity model", ) print('ModelPackageGroup Arn : {}'.format(create_model_package_group_response['ModelPackageGroupArn']))

Scripts 118
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How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

AWS Machine Learning

Data preparation and training The data preparation and training pipeline includes the following steps: The training data is read from a PrestoDB instance, and any feature engineering needed is done as part of the SQL queries run in PrestoDB at retrieval time. For more information on processing jobs, see Process data.

Scripts 116
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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

Depending on the design of your feature groups and their scale, you can experience training query performance improvements of 10x to 100x by using this new capability. The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. Creating feature groups using Iceberg table format.

Scripts 84