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Drive efficiencies with CI/CD best practices on Amazon Lex

AWS Machine Learning

You liked the overall experience and now want to deploy the bot in your production environment, but aren’t sure about best practices for Amazon Lex. In this post, we review the best practices for developing and deploying Amazon Lex bots, enabling you to streamline the end-to-end bot lifecycle and optimize your operations.

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Best practices for viewing and querying Amazon SageMaker service quota usage

AWS Machine Learning

In addition, they can view near real-time utilization metrics and create Amazon CloudWatch metrics to view and programmatically query SageMaker quotas. With Service Quotas, you can view the maximum number of resources, actions, or items in your AWS account or AWS Region. For instructions, refer to Secure Your AWS Account.

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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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Establishing an AI/ML center of excellence

AWS Machine Learning

They establish and enforce best practices encompassing design, development, processes, and governance operations, thereby mitigating risks and making sure robust business, technical, and governance frameworks are consistently upheld.

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Prevent account takeover at login with the new Account Takeover Insights model in Amazon Fraud Detector

AWS Machine Learning

So much exposure naturally brings added risks like account takeover (ATO). Each year, bad actors compromise billions of accounts through stolen credentials, phishing, social engineering, and multiple forms of ATO. To put it into perspective: account takeover fraud increased by 90% to an estimated $11.4

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

AWS Machine Learning

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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Customer Success vs. Account Management: Why Both Matter

Help Scout

When considering customer success and account management, it’s valuable to consider them as complementary teams rather than interchangeable groups or, worse, competitors. There are a few things that differentiate customer success from account management.