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Security best practices to consider while fine-tuning models in Amazon Bedrock

AWS Machine Learning

In this post, we delve into the essential security best practices that organizations should consider when fine-tuning generative AI models. Analyze results through metrics and evaluation. Security in Amazon Bedrock Cloud security at AWS is the highest priority. Set up IAM permissions for data access. Configure a KMS key and VPC.

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Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines

AWS Machine Learning

In this post, we provide some best practices to maximize the value of SageMaker Pipelines and make the development experience seamless. Best practices for SageMaker Pipelines In this section, we discuss some best practices that can be followed while designing workflows using SageMaker Pipelines.

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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Source: Human Resource Management; Issue: 51(4); 2012; Pages 535-548. Metrics, Measure, and Monitor – Make sure your metrics and associated goals are clear and concise while aligning with efficiency and effectiveness. Make each metric public and ensure everyone knows why that metric is measured. Jeff Greenfield.

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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

For an example account structure to follow organizational unit best practices to host models using SageMaker endpoints across accounts, refer to MLOps Workload Orchestrator. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices. Prerequisites.

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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

Policy 3 – Attach AWSLambda_FullAccess , which is an AWS managed policy that grants full access to Lambda, Lambda console features, and other related AWS services.

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Detect and protect sensitive data with Amazon Lex and Amazon CloudWatch Logs

AWS Machine Learning

One risk many organizations face is the inadvertent exposure of sensitive data through logs, voice chat transcripts, and metrics. It’s a best practice to identify and mark all slots that could potentially capture PII during the bot design phase to provide comprehensive protection across the conversation flow.

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AI-powered code suggestions and security scans in Amazon SageMaker notebooks using Amazon CodeWhisperer and Amazon CodeGuru

AWS Machine Learning

These AI-powered extensions help accelerate ML development by offering code suggestions as you type, and ensure that your code is secure and follows AWS best practices. Today, we are excited to announce the availability of Amazon CodeWhisperer and Amazon CodeGuru Security extensions in SageMaker notebooks.