article thumbnail

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

AWS Machine Learning

Who needs a cross-account feature store Organizations need to securely share features across teams to build accurate ML models, while preventing unauthorized access to sensitive data. SageMaker Feature Store now allows granular sharing of features across accounts via AWS RAM, enabling collaborative model development with governance.

article thumbnail

­­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. Next, you need to create a Python script to run the Iceberg procedures. AWS Glue Job setup.

Scripts 73
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

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

AWS Machine Learning

default_bucket() upload _path = f"training data/fhe train.csv" boto3.Session().resource("s3").Bucket To see more information about natively supported frameworks and script mode, refer to Use Machine Learning Frameworks, Python, and R with Amazon SageMaker. resource("s3").Bucket Bucket (bucket).Object Object (upload path).upload

Scripts 95
article thumbnail

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.

article thumbnail

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. Information that identifies you may be shared with doctors responsible for your care or for audits and evaluations by government agencies, but talks and papers about the study will not identify you.

APIs 113
article thumbnail

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
article thumbnail

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. We also introduce a logical construct of a shared services account that plays a key role in governance, administration, and orchestration.

APIs 70