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

AWS Machine Learning

SageMaker Feature Store now allows granular sharing of features across accounts via AWS RAM, enabling collaborative model development with governance. For example, the analytics team may curate features like customer profile, transaction history, and product catalogs in a central management account.

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

AWS Machine Learning

Homomorphic encryption is a new approach to encryption that allows computations and analytical functions to be run on encrypted data, without first having to decrypt it, in order to preserve privacy in cases where you have a policy that states data should never be decrypted. . resource("s3").Bucket Bucket (bucket).Object

Scripts 94
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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. Apache Iceberg is an open table format for very large analytic datasets. AWS Glue Job setup.

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

AWS Machine Learning

Consider your security posture, governance, and operational excellence when assessing overall readiness to develop generative AI with LLMs and your organizational resiliency to any potential impacts. Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

This data is information rich but can be vastly heterogenous. Proper handling of specialized terminology and concepts in different formats is essential to detect insights and ensure analytical integrity. With Knowledge Bases for Amazon Bedrock, you can access detailed information through simple, natural queries.

APIs 111
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The State of the Bot Going Into 2018

Aspect

RFPs for chatbots have arisen in verticals as diverse as banking, government, healthcare, and retail. In 2018, we should see much better integration with customer data and analytics, bringing customer history, behavioral patterns, and big data into chatbot interactions.

Chatbots 116
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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. Victor is an engineer and scrum master, helping data scientists build secure fast machine learning pipelines.