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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

This streamlines the ML workflows, enables better visibility and governance, and accelerates the adoption of ML models across the organization. Before we dive into the details of the architecture for sharing models, let’s review what use case and model governance is and why it’s needed.

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Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning

Model governance – The Amazon SageMaker Model Registry integration allows for tracking model versions, and therefore promoting them to production with confidence. You can then iterate on preprocessing, training, and evaluation scripts, as well as configuration choices. script is used by pipeline_service.py The model_unit.py

Scripts 117
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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

Who needs a cross-account feature store Organizations need to securely share features across teams to build accurate ML models, while preventing unauthorized access to sensitive data. SageMaker Feature Store now allows granular sharing of features across accounts via AWS RAM, enabling collaborative model development with governance.

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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

AWS Machine Learning

When you open a notebook in Studio, you are prompted to set up your environment by choosing a SageMaker image, a kernel, an instance type, and, optionally, a lifecycle configuration script that runs on image startup. You can implement comprehensive tests, governance, security guardrails, and CI/CD automation to produce custom app images.

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

AWS Machine Learning

Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table. Next, you need to create a Python script to run the Iceberg procedures. AWS Glue Job setup.

Scripts 85
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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

default_bucket() upload _path = f"training data/fhe train.csv" boto3.Session().resource("s3").Bucket To see more information about natively supported frameworks and script mode, refer to Use Machine Learning Frameworks, Python, and R with Amazon SageMaker. resource("s3").Bucket Bucket (bucket).Object Object (upload path).upload

Scripts 119
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Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. We also introduce a logical construct of a shared services account that plays a key role in governance, administration, and orchestration.

APIs 81