This post was co-authored with Johann Wildgruber, Dr. Jens Kohl, Thilo Bindel, and Luisa-Sophie Gloger from BMW Group. The BMW Group—headquartered in Munich, Germany—is a vehicle manufacturer with more than 154,000 employees, and 30 production and assembly facilities worldwide as well...

Generative AI question-answering applications are pushing the boundaries of enterprise productivity. These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. However,...

Time series forecasting is critical for decision-making across industries. From predicting traffic flow to sales forecasting, accurate predictions enable organizations to make informed decisions, mitigate risks, and allocate resources efficiently. However, traditional machine learning approaches often require extensive data-specific...

Amazon Bedrock Knowledge Bases offers a fully managed Retrieval Augmented Generation (RAG) feature that connects large language models (LLMs) to internal data sources. It’s a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in...

Building cloud infrastructure based on proven best practices promotes security, reliability and cost efficiency. To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. As systems scale, conducting thorough AWS Well-Architected Framework...

Increasingly, organizations across industries are turning to generative AI foundation models (FMs) to enhance their applications. To achieve optimal performance for specific use cases, customers are adopting and adapting these FMs to their unique domain requirements. This need for...