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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 112
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Fine-tune Anthropic’s Claude 3 Haiku in Amazon Bedrock to boost model accuracy and quality

AWS Machine Learning

This process enhances task-specific model performance, allowing the model to handle custom use cases with task-specific performance metrics that meet or surpass more powerful models like Anthropic Claude 3 Sonnet or Anthropic Claude 3 Opus. Under Output data , for S3 location , enter the S3 path for the bucket storing fine-tuning metrics.

APIs 122
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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.

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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 77
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Fireside Chat: Natero Shares Tips for Customer Success

Satrix Solutions

Natero helps Customer Success Managers reduce churn, increase expansion, and manage more accounts. Read our interview: Evan Klein: How has the Customer Success industry changed since Natero was first founded in 2012? What Customer Success metrics / key performance indicators are most important to your customers?

SaaS 60
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Cautionary tale—BNPL disrupts with faster, smarter, more personalized payment solutions

Maru Group

As early as 2012, fintechs provided a viable alternative to traditional financial institutions for purchases ranging from home appliances to travel packages. The distrust stems from the 2001 Corralito policies in Argentina restricting people’s ability to withdraw cash from their accounts. Ready to jumpstart your CX program?