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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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­­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 80
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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 119
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Before you can write scripts that use the Amazon Bedrock API, you’ll need to install the appropriate version of the AWS SDK in your environment. He has helped companies in many industries, including insurance, financial services, media and entertainment, healthcare, utilities, and manufacturing. Nihir Chadderwala is a Sr.

APIs 130
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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 114
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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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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. These had to be manually tracked with detailed instructions on how each output would be used in subsequent processes.