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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock empowers teams to generate Terraform and CloudFormation scripts that are custom fitted to organizational needs while seamlessly integrating compliance and security best practices. This makes sure your cloud foundation is built according to AWS best practices from the start.

Scripts 129
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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

Challenges in data management Traditionally, managing and governing data across multiple systems involved tedious manual processes, custom scripts, and disconnected tools. As producers, data engineers in these accounts are responsible for creating, transforming, and managing data assets that will be cataloged and governed by Amazon DataZone.

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Struggling With How to Generate Leads? Use These Best Practices

Quality Contact Solutions

That’s why we’ve compiled four best practices to help you meet your sales goals and keep your team busy. Our next lead generation best practice is customer service. Our next best practice in how to generate leads is to focus on your website. Case Study: B2B Lead Generation & Cold Calling.

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Best practices to build generative AI applications on AWS

AWS Machine Learning

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

For early detection, implement custom testing scripts that run toxicity evaluations on new data and model outputs continuously. Integrating scheduled toxicity assessments and custom testing scripts into your development pipeline helps you continuously monitor and adjust model behavior.

APIs 111
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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

AWS Machine Learning

This work extends upon the post Generating value from enterprise data: Best practices for Text2SQL and generative AI. The top-level definitions of these abstractions are included as part of the prompt context for query generation, and the full definitions are provided to the SQL execution engine, along with the generated query.

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Best practices for Amazon SageMaker Training Managed Warm Pools

AWS Machine Learning

In this post, we outline the key benefits and pain points addressed by SageMaker Training Managed Warm Pools, as well as benchmarks and best practices. Guidance on what input mode to select is in the best practices section later in this post. Best practices for using warm pools. Data Input Mode.