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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning Blog

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.

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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 3

AWS Machine Learning Blog

We show you how to use AWS IoT Greengrass to manage model inference at the edge and how to automate the process using AWS Step Functions and other AWS services. AWS IoT Greengrass is an Internet of Things (IoT) open-source edge runtime and cloud service that helps you build, deploy, and manage edge device software.

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How OCX Cognition reduced ML model development time from weeks to days and model update time from days to real time using AWS Step Functions and Amazon SageMaker

AWS Machine Learning Blog

OCX’s solutions are developed in collaboration with Infogain , an AWS Advanced Tier Partner. Infogain works with OCX Cognition as an integrated product team, providing human-centered software engineering services and expertise in software development, microservices, automation, Internet of Things (IoT), and artificial intelligence.

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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 1

AWS Machine Learning Blog

However, using purpose-built services like Amazon SageMaker and AWS IoT Greengrass allows you to significantly reduce this effort. If you’re just getting started with MLOps at the edge on AWS, refer to MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass for an overview and reference architecture.

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Unlock the potential of generative AI in industrial operations

AWS Machine Learning Blog

PandasAI is a Python library that adds generative AI capabilities to pandas, the popular data analysis and manipulation tool. However, complex NLQs, such as time series data processing, multi-level aggregation, and pivot or joint table operations, may yield inconsistent Python script accuracy with a zero-shot prompt.

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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

AWS Machine Learning Blog

On top of that, the whole process can be configured and managed via the AWS SDK, which is what we use to orchestrate our labeling workflow as part of our CI/CD pipeline. For more information about best practices, refer to the AWS re:Invent 2019 talk, Build accurate training datasets with Amazon SageMaker Ground Truth.

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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning Blog

With AWS generative AI services like Amazon Bedrock , developers can create systems that expertly manage and respond to user requests. It is hosted on Amazon Elastic Container Service (Amazon ECS) with AWS Fargate , and it is accessed using an Application Load Balancer. It serves as the data source to the knowledge base.

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