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The Impact of Conversational AI on Healthcare Outcomes and Patient Satisfaction

JustCall

The digital revolution has left an imprint on the healthcare industry as well. As a result, we are witnessing the technological integration of Big Data, Artificial Intelligence, Machine Learning, the Internet of Things, etc., with healthcare. with healthcare. What is Conversational AI in Healthcare?

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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning

Large language models (LLMs) are revolutionizing fields like search engines, natural language processing (NLP), healthcare, robotics, and code generation. Another essential component is an orchestration tool suitable for prompt engineering and managing different type of subtasks. A feature store maintains user profile data.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring. The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. Conclusion.

Scripts 73
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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

AWS Machine Learning

The data distribution for punt and kickoff are different. Data preprocessing and feature engineering First, the tracking data was filtered for just the data related to punts and kickoff returns. As a baseline, we used the model that won our NFL Big Data Bowl competition on Kaggle.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

We demonstrate CDE using simple examples and provide a step-by-step guide for you to experience CDE in an Amazon Kendra index in your own AWS account. The image repository is then indexed by Amazon Kendra, which is a search engine that can be used to search for structured and unstructured data.