Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

Favorite This is a guest post co-written with Babu Srinivasan from MongoDB. As industries evolve in today’s fast-paced business landscape, the inability to have real-time forecasts poses significant challenges for industries heavily reliant on accurate and timely insights. The absence of real-time forecasts in various industries presents pressing business challenges

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Shared by AWS Machine Learning December 19, 2023

Advancements in machine learning for machine learning

Favorite Posted by Phitchaya Mangpo Phothilimthana, Staff Research Scientist, Google DeepMind, and Bryan Perozzi, Senior Staff Research Scientist, Google Research With the recent and accelerated advances in machine learning (ML), machines can understand natural language, engage in conversations, draw images, create videos and more. Modern ML models are programmed and

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Shared by Google AI Technology December 15, 2023

StyleDrop: Text-to-image generation in any style

Favorite Posted by Kihyuk Sohn and Dilip Krishnan, Research Scientists, Google Research Text-to-image models trained on large volumes of image-text pairs have enabled the creation of rich and diverse images encompassing many genres and themes. Moreover, popular styles such as “anime” or “steampunk”, when added to the input text prompt,

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Shared by Google AI Technology December 15, 2023

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