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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

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Amazon Q Business , a new generative AI-powered assistant, can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in an enterprises systems. Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management.

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Secure a generative AI assistant with OWASP Top 10 mitigation

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This comprehensive security setup addresses LLM10:2025 Unbound Consumption and LLM02:2025 Sensitive Information Disclosure, making sure that applications remain both resilient and secure. In the physical architecture diagram, the application controller is the LLM orchestrator AWS Lambda function.

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Boosting Resiliency with an ML-based Telemetry Analytics Architecture | Amazon Web Services

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Data proliferation has become a norm and as organizations become more data driven, automating data pipelines that enable data ingestion, curation, …

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Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

AI News

If you think about building a data pipeline, whether you’re doing a simple BI project or a complex AI or machine learning project, you’ve got data ingestion, data storage and processing, and data insight – and underneath all of those four stages, there’s a variety of different technologies being used,” explains Faruqui.

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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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Create a next generation chat assistant with Amazon Bedrock, Amazon Connect, Amazon Lex, LangChain, and WhatsApp

AWS Machine Learning Blog

Amazon Bedrock Knowledge Bases gives foundation models (FMs) and agents contextual information from your company’s private data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, accurate, and customized responses. The outbound message handler retrieves the relevant chat contact information from Amazon DynamoDB.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Thats why we use advanced technology and data analytics to streamline every step of the homeownership experience, from application to closing. Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks.