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

AWS Machine Learning

Deploy the solution The application presented in this post is available in the accompanying GitHub repository and provided as an AWS Cloud Development Kit (AWS CDK) project. Run the script init-script.bash : chmod u+x init-script.bash./init-script.bash The script deploys the AWS CDK project in your account.

APIs 124
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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. For example, to use the RedPajama dataset, use the following command: wget [link] python nemo/scripts/nlp_language_modeling/preprocess_data_for_megatron.py

Scripts 127
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

from time import gmtime, strftime experiment_suffix = strftime('%d-%H-%M-%S', gmtime()) experiment_name = f"credit-risk-model-experiment-{experiment_suffix}" The processing script creates a new MLflow active experiment by calling the mlflow.set_experiment() method with the experiment name above. fit_transform(y).

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Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning

In this post, we present a framework for automating the creation of a directed acyclic graph (DAG) for Amazon SageMaker Pipelines based on simple configuration files. The framework code and examples presented here only cover model training pipelines, but can be readily extended to batch inference pipelines as well. The model_unit.py

Scripts 127
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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

It also provides common ML algorithms that are optimized to run efficiently against extremely large data in a distributed environment. We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket.

Scripts 133
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Customer Experience Automation: Transforming the Future of Customer Service

TechSee

Today, CXA encompasses various technologies such as AI, machine learning, and big data analytics to provide personalized and efficient customer experiences. This lack of integration can result in data silos, where valuable customer information is not shared across departments, hindering the ability to provide a cohesive service.

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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

AWS Machine Learning

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. A public GitHub repo provides hands-on examples for each of the presented approaches. In the Scripts section, define the script to be run when the kernel starts.