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Cohere Embed multimodal embeddings model is now available on Amazon SageMaker JumpStart

AWS Machine Learning Blog

It provides a common framework for assessing the performance of natural language processing (NLP)-based retrieval models, making it straightforward to compare different approaches. It offers an unparalleled suite of tools that cater to every stage of the ML lifecycle, from data preparation to model deployment and monitoring.

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Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock

AWS Machine Learning Blog

Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI , allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. This process involves updating the model’s weights to improve its performance on targeted applications.

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The AI Process

Towards AI

AI engineering is the discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts, which combines the principles of systems engineering, software engineering, and computer science to create AI systems.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Deep Learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, Natural Language Processing, and speech recognition. Natural Language Processing (NLP) This is a field of computer science that deals with the interaction between computers and human language.

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Build well-architected IDP solutions with a custom lens – Part 2: Security

AWS Machine Learning Blog

An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific entities or phrases. She has extensive experience in machine learning with a PhD degree in computer science.

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

AWS Machine Learning Blog

We create an automated model build pipeline that includes steps for data preparation, model training, model evaluation, and registration of the trained model in the SageMaker Model Registry. Romina’s areas of interest are natural language processing, large language models, and MLOps.

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Unlocking efficiency: Harnessing the power of Selective Execution in Amazon SageMaker Pipelines

AWS Machine Learning Blog

It simplifies the development and maintenance of ML models by providing a centralized platform to orchestrate tasks such as data preparation, model training, tuning and validation. SageMaker Pipelines can help you streamline workflow management, accelerate experimentation and retrain models more easily. Nishant Krishnamoorthy is a Sr.

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