Manufacturing intelligence with Amazon Nova Multimodal Embeddings
📰 AWS Machine Learning
Learn to build a multimodal retrieval system for manufacturing documents using Amazon Nova Multimodal Embeddings and evaluate its performance against a text-only pipeline
Action Steps
- Build a multimodal retrieval system using Amazon Nova Multimodal Embeddings on Amazon Bedrock and Amazon S3 Vectors
- Evaluate the system on manufacturing queries
- Compare generation quality between a text-only pipeline and the multimodal pipeline
- Configure Amazon S3 Vectors for storing and querying multimodal embeddings
- Test the system on 26 manufacturing queries to assess its performance
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this tutorial to improve their skills in building multimodal retrieval systems, while product managers can understand the potential applications of such systems in manufacturing intelligence
Key Insight
💡 Multimodal retrieval systems can outperform text-only pipelines in certain applications, such as manufacturing intelligence
Share This
Build a multimodal retrieval system for manufacturing docs with Amazon Nova Multimodal Embeddings! #AWSMachineLearning #MultimodalRetrieval
Key Takeaways
Learn to build a multimodal retrieval system for manufacturing documents using Amazon Nova Multimodal Embeddings and evaluate its performance against a text-only pipeline
Full Article
In this post, we build a multimodal retrieval system for aerospace manufacturing documents using Amazon Nova Multimodal Embeddings on Amazon Bedrock and Amazon S3 Vectors. We evaluate the system on 26 manufacturing queries and compare generation quality between a text-only pipeline and the multimodal pipeline.
DeepCamp AI