Remove Big data Remove Healthcare Remove Metrics Remove Scripts
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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. 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.

Scripts 73
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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

Under Advanced Project Options , for Definition , select Pipeline script from SCM. For Script Path , enter Jenkinsfile. upload_file("pipelines/train/scripts/raw_preprocess.py","mammography-severity-model/scripts/raw_preprocess.py") s3_client.Bucket(default_bucket).upload_file("pipelines/train/scripts/evaluate_model.py","mammography-severity-model/scripts/evaluate_model.py")

Scripts 98
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Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

AWS Machine Learning

Each project maintained detailed documentation that outlined how each script was used to build the final model. In many cases, this was an elaborate process involving 5 to 10 scripts with several outputs each. For logging, we utilize FluentD to push all our container logs to Amazon OpenSearch Service and system metrics to Prometheus.

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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.

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Create SageMaker Pipelines for training, consuming and monitoring your batch use cases

AWS Machine Learning

The code sets up the S3 paths for pipeline inputs, outputs, and model artifacts, and uploads scripts used within the pipeline steps. This step uses the built-in ProcessingStep with the provided code, evaluation.py , to evaluate performance metrics (accuracy, area under curve). Repeat the same for the second custom policy.

Metrics 73
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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

This is backed by our deep set of over 300 cloud security tools and the trust of our millions of customers, including the most security-sensitive organizations like government, healthcare, and financial services. Ram Vittal is a Principal ML Solutions Architect at AWS.