AWS Document Internet of Things 
AWS Machine Learning Blog
OCTOBER 31, 2024
AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.
AWS Machine Learning Blog
MARCH 17, 2025
AWS Lambda orchestrator, along with tool configuration and prompts, handles orchestration and invokes the Mistral model on Amazon Bedrock. Data is stored in a conversation history, and a member database (MemberDB) is used to store member information and the knowledge base has static documents used by the agent.
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JULY 7, 2025
This solution extends the capabilities demonstrated in Automate chatbot for document and data retrieval using Amazon Bedrock Agents and Knowledge Bases , which discussed using Amazon Bedrock Agents for document and data retrieval. The solution uses the AWS Cloud Development Kit (AWS CDK) to deploy the solution components.
AWS Machine Learning Blog
JANUARY 30, 2025
The AWS Social Responsibility & Impact (SRI) team recognized an opportunity to augment this function using generative AI. Historically, AWS Health Equity Initiative applications were reviewed manually by a review committee. Review the provided proposal document: {PROPOSAL} 2. Here are the steps to follow: 1.
Dataconomy
JUNE 18, 2025
IoT data monitoring Data integration is crucial for processing Internet of Things (IoT) data, enabling predictive maintenance and operational efficiency through real-time insights. Documentation of data architecture Thorough documentation of data systems architecture is crucial for effective integration and long-term maintenance.
APRIL 21, 2025
In this post, to address the aforementioned challenges, we introduce an automated evaluation framework that is deployable on AWS. We then present a typical evaluation workflow, followed by our AWS-based solution that facilitates this process. The UI service can be run locally in a Docker container or deployed to AWS Fargate.
AWS Machine Learning Blog
JULY 11, 2025
Pre-training is highly resource-intensive, requiring substantial compute (often across thousands of GPUs or AWS Trainium chips), large-scale distributed training frameworks, and careful data curation to balance performance with bias, safety, and accuracy concerns. The following table summarizes the different types of PEFT.
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