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

AWS Machine Learning Blog

The need for federated learning in healthcare Healthcare relies heavily on distributed data sources to make accurate predictions and assessments about patient care. Limiting the available data sources to protect privacy negatively affects result accuracy and, ultimately, the quality of patient care.

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Governing the ML lifecycle at scale: Centralized observability with Amazon SageMaker and Amazon CloudWatch

AWS Machine Learning Blog

You can use cross-account observability in CloudWatch to search, analyze, and correlate cross-account telemetry data stored in CloudWatch such as metrics, logs, and traces from one centralized account. You can now set up a central observability AWS account and connect your other accounts as sources.

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How to Build ETL Data Pipeline in ML

The MLOps Blog

These pipelines assist data scientists in saving time and effort by ensuring that the data is clean, properly formatted, and ready for use in machine learning tasks. Moreover, ETL pipelines play a crucial role in breaking down data silos and establishing a single source of truth.

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Simplify data access for your enterprise using Amazon SageMaker Lakehouse

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Currently, organizations often create custom solutions to connect these systems, but they want a more unified approach that them to choose the best tools while providing a streamlined experience for their data teams. You can use Amazon SageMaker Lakehouse to achieve unified access to data in both data warehouses and data lakes.

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Snowflake Summit 2022: 3 Key Takeaways from Alation

Alation

To support this initiative, the Travelers team created a Data Culture Map. This map shows how data culture and data literacy can transform an organization. Snowflake is key to this journey, as it brings all their data together into one landscape. Download our joint solution brief today.