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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

To address the problems associated with complex searches, this post describes in detail how you can achieve a search engine that is capable of searching for complex images by integrating Amazon Kendra and Amazon Rekognition. A Python script is used to aid in the process of uploading the datasets and generating the manifest file.

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Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

AWS Machine Learning

With these updates to SMP’s APIs, you can now realize the performance benefits of SageMaker and the SMP library without overhauling your existing PyTorch FSDP training scripts. For more information on how to enable SMP with your existing PyTorch FSDP training scripts, refer to Get started with SMP.

Scripts 101
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What Is Knowledge Engineering and Why Do I Need It for Chatbot Development?

Aspect

It also has to be engineered to fit different purposes and contexts. No, there are simple, static bots that can be developed with scripting tools. However, some general principles for developing bots need to be considered, whether using knowledge engineering or not. appeared first on Aspect Blogs.

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Build Streamlit apps in Amazon SageMaker Studio

AWS Machine Learning

The code for this blog can be found in this GitHub repository. Optional) Create a Shared Space We can use the SageMaker console or the AWS CLI to add support for shared spaces to an existing Domain by following the steps in the docs or in this blog. The code for this blog can be found in this GitHub repository. sh setup.sh

Scripts 73
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Everything You Need to Know About Auto Attendant

Hodusoft

In this blog post, we will discuss everything about auto attendants starting from what they are, how they work, the pros and cons of using auto attendants, how to set up an auto attendant, and how to include scripts. Pros of Using Auto Attendant Cons of Auto Attendant Auto Attendant Scripts – What to Record?

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Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together

AWS Machine Learning

Media organizations can generate image captions or video scripts automatically. Imagine sophisticated conversational assistants providing fast and highly-contextual responses, picture personalized recommendation engines that seamlessly blend in relevant images, diagrams and associated knowledge to intuitively guide decisions.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning

Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts. Ideally, we instead want to load the model PyTorch scripts, extract the features from model input, and run model inference entirely in Java. They use the DJL PyTorch engine to initialize the model predictor.