Developing robust text-to-SQL capabilities is a critical challenge in the field of natural language processing (NLP) and database management. The complexity of NLP and database management increases in this field, particularly while dealing with complex queries and database structures. In this post, we introduce a straightforward but powerful solution withContinue Reading
We’re excited to announce that Amazon Bedrock Custom Model Import now supports Qwen models. You can now import custom weights for Qwen2, Qwen2_VL, and Qwen2_5_VL architectures, including models like Qwen 2, 2.5 Coder, Qwen 2.5 VL, and QwQ 32B. You can bring your own customized Qwen models into Amazon Bedrock and deploy them in a fullyContinue Reading
Businesses are constantly evolving, and leaders are challenged every day to meet new requirements and are seeking ways to optimize their operations and gain a competitive edge. One of the key challenges they face is managing the complexity of disparate business systems and workflows, which leads to inefficiencies, data silos,Continue Reading
With Amazon Bedrock Evaluations, you can evaluate foundation models (FMs) and Retrieval Augmented Generation (RAG) systems, whether hosted on Amazon Bedrock or another model or RAG system hosted elsewhere, including Amazon Bedrock Knowledge Bases or multi-cloud and on-premises deployments. We recently announced the general availability of the large language modelContinue Reading
Retrieval Augmented Generation (RAG) enhances AI responses by combining the generative AI model’s capabilities with information from external data sources, rather than relying solely on the model’s built-in knowledge. In this post, we showcase the custom data connector capability in Amazon Bedrock Knowledge Bases that makes it straightforward to buildContinue Reading
Many organizations rely on multiple third-party applications and services for different aspects of their operations, such as scheduling, HR management, financial data, customer relationship management (CRM) systems, and more. However, these systems often exist in silos, requiring users to manually navigate different interfaces, switch between environments, and perform repetitive tasks,Continue Reading
When implementing machine learning (ML) workflows in Amazon SageMaker Canvas, organizations might need to consider external dependencies required for their specific use cases. Although SageMaker Canvas provides powerful no-code and low-code capabilities for rapid experimentation, some projects might require specialized dependencies and libraries that aren’t included by default in SageMakerContinue Reading
In enterprise environments, organizations often divide their AI operations into two specialized teams: an AI research team and a model hosting team. The research team is dedicated to developing and enhancing AI models using model training and fine-tuning techniques. Meanwhile, a separate hosting team is responsible for deploying these modelsContinue Reading
This blog post is co-written with Gene Arnold from Alation. To build a generative AI-based conversational application integrated with relevant data sources, an enterprise needs to invest time, money, and people. First, you would need build connectors to the data sources. Next you need to index this data to makeContinue Reading
Open foundation models (FMs) have become a cornerstone of generative AI innovation, enabling organizations to build and customize AI applications while maintaining control over their costs and deployment strategies. By providing high-quality, openly available models, the AI community fosters rapid iteration, knowledge sharing, and cost-effective solutions that benefit both developersContinue Reading
