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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Run the following code in the ML Shared Services account (Account A).

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

AWS Machine Learning

An AWS account and an AWS Identity and Access Management (IAM) principal with sufficient permissions to create and manage the resources needed for this application. 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.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product. To view this series from the beginning, start with Part 1.

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Fine-tune and deploy a summarizer model using the Hugging Face Amazon SageMaker containers bringing your own script

AWS Machine Learning

Pre-trained models and fully managed NLP services have democratised access and adoption of NLP. Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data without the need of any prior ML experience.

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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

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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. You can manage app images via the SageMaker console, the AWS SDK for Python (Boto3), and the AWS Command Line Interface (AWS CLI).

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

AWS Machine Learning

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Store your Snowflake account credentials in AWS Secrets Manager.

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