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AI Workforce: using AI and Drones to simplify infrastructure inspections

AWS Machine Learning

Security is paramount, and we adhere to AWS best practices across the layers. Each drone follows predefined routes, with flight waypoints, altitude, and speed configured through an AWS API, using coordinates stored in Amazon DynamoDB. The following diagram outlines how different components interact.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.

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Best practices for building secure applications with Amazon Transcribe

AWS Machine Learning

Because these best practices might not be appropriate or sufficient for your environment, use them as helpful considerations rather than prescriptions. Applications must have valid credentials to sign API requests to AWS services. The customer data is cleaned up for both complete and failure cases.

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Maximizing ROI with CPQ: 10 Best Practices for Sales Success

Cincom

This article outlines 10 CPQ best practices to help optimize your performance, eliminate inefficiencies, and maximize ROI. Use APIs and middleware to bridge gaps between CPQ and existing enterprise systems, ensuring smooth data flow. Implement event-driven architecture where updates in CRM (e.g.,

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Generating value from enterprise data: Best practices for Text2SQL and generative AI

AWS Machine Learning

In this post, we provide an introduction to text to SQL (Text2SQL) and explore use cases, challenges, design patterns, and best practices. Today, a large amount of data is available in traditional data analytics, data warehousing, and databases, which may be not easy to query or understand for the majority of organization members.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process. It functions as a standalone HTTP server that provides various REST API endpoints for monitoring, recording, and visualizing experiment runs. We specifically focus on SageMaker with MLflow.