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

AWS Machine Learning

Analyze results through metrics and evaluation. The workflow steps are as follows: The user submits an Amazon Bedrock fine-tuning job within their AWS account, using IAM for resource access. The fine-tuning job initiates a training job in the model deployment accounts. Provide your account, bucket name, and VPC settings.

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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

When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices.

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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

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts 122
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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning

Prerequisites To implement the proposed solution, make sure that you have the following: An AWS account and a working knowledge of FMs, Amazon Bedrock , Amazon SageMaker , Amazon OpenSearch Service , Amazon S3 , and AWS Identity and Access Management (IAM). Distance metric : Select Euclidean. Engine : Select nmslib. Choose Confirm.

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Deep demand forecasting with Amazon SageMaker

AWS Machine Learning

The input data is a multi-variate time series that includes hourly electricity consumption of 321 users from 2012–2014. Amazon Forecast is a time-series forecasting service based on machine learning (ML) and built for business metrics analysis. If you don’t have an account, you can sign up for one. Solution overview.

Metrics 94
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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. For example, you may have the following data types: Name Address Phone number Email address Account number Email address and physical mailing address are often considered a medium classification level.