Improve your Stable Diffusion prompts with Retrieval Augmented Generation

Favorite Text-to-image generation is a rapidly growing field of artificial intelligence with applications in a variety of areas, such as media and entertainment, gaming, ecommerce product visualization, advertising and marketing, architectural design and visualization, artistic creations, and medical imaging. Stable Diffusion is a text-to-image model that empowers you to create

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Shared by AWS Machine Learning December 15, 2023

Automate PDF pre-labeling for Amazon Comprehend

Favorite Amazon Comprehend is a natural-language processing (NLP) service that provides pre-trained and custom APIs to derive insights from textual data. Amazon Comprehend customers can train custom named entity recognition (NER) models to extract entities of interest, such as location, person name, and date, that are unique to their business.

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Shared by AWS Machine Learning December 15, 2023

How AWS Prototyping enabled ICL-Group to build computer vision models on Amazon SageMaker

Favorite This is a customer post jointly authored by ICL and AWS employees. ICL is a multi-national manufacturing and mining corporation based in Israel that manufactures products based on unique minerals and fulfills humanity’s essential needs, primarily in three markets: agriculture, food, and engineered materials. Their mining sites use industrial

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Shared by AWS Machine Learning December 15, 2023

Boost productivity on Amazon SageMaker Studio: Introducing JupyterLab Spaces and generative AI tools

Favorite Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. It provides access to the most comprehensive set of tools for each step of ML development,

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Shared by AWS Machine Learning December 15, 2023

Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions

Favorite Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Building

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Shared by AWS Machine Learning December 14, 2023