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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

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts 95
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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Table formats provide a way to abstract data files as a table. You can find the sample script in GitHub.

Scripts 72
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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. default_bucket() upload _path = f"training data/fhe train.csv" boto3.Session().resource("s3").Bucket resource("s3").Bucket

Scripts 91
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Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

AWS Machine Learning

Access to AWS services from Katib and from pipeline pods using the AWS IAM Roles for Service Accounts (IRSA) integration with Kubeflow Profiles. Each project maintained detailed documentation that outlined how each script was used to build the final model. AWS Secrets Manager to protect secrets needed to access your applications.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

In addition to awareness, your teams should take action to account for generative AI in governance, assurance, and compliance validation practices. You should begin by extending your existing security, assurance, compliance, and development programs to account for generative AI.

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Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition

AWS Machine Learning

Prerequisites For this walkthrough, you should have the following prerequisites: An AWS account. Two components need to be configured in our inference script : model loading and model serving. Aamna Najmi is a Data Scientist with AWS Professional Services. iterdir(): if p_file.suffix == ".pth":

APIs 62
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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning

However, the sharing of raw, non-sanitized sensitive information across different locations poses significant security and privacy risks, especially in regulated industries such as healthcare. Limiting the available data sources to protect privacy negatively affects result accuracy and, ultimately, the quality of patient care.