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​ Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more. With access to massive amounts… Read More »Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart Xin Huang AWS Machine Learning Blog

​ Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more. With access to massive amounts… Read More »Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart Xin Huang AWS Machine Learning Blog

​ Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more. With access to massive amounts… Read More »Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart Xin Huang AWS Machine Learning Blog

​ Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more. With access to massive amounts… Read More »Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart Xin Huang AWS Machine Learning Blog

​ Amazon Kendra is an intelligent search service powered by machine learning (ML), enabling organizations to provide relevant information to customers and employees, when they need it. Amazon Kendra uses ML algorithms to enable users to use natural language queries to search for information scattered… Read More »Announcing the updated Microsoft OneDrive connector (V2) for Amazon Kendra Pravinchandra Varma AWS Machine Learning Blog

​ Amazon Kendra is an intelligent search service powered by machine learning (ML), enabling organizations to provide relevant information to customers and employees, when they need it. Amazon Kendra uses ML algorithms to enable users to use natural language queries to search for information scattered… Read More »Announcing the updated Microsoft OneDrive connector (V2) for Amazon Kendra Pravinchandra Varma AWS Machine Learning Blog

​ This post was co-written with Dave Gowel, CEO of RallyPoint. In his own words, “RallyPoint is an online social and professional network for veterans, service members, family members, caregivers, and other civilian supporters of the US armed forces. With two million members on the… Read More »How RallyPoint and AWS are personalizing job recommendations to help military veterans and service providers transition back into civilian life using Amazon Personalize Dave Gowel AWS Machine Learning Blog

​ Reliability managers and technicians in industrial environments such as manufacturing production lines, warehouses, and industrial plants are keen to improve equipment health and uptime to maximize product output and quality. Machine and process failures are often addressed by reactive activity after incidents happen or… Read More »Generate actionable insights for predictive maintenance management with Amazon Monitron and Amazon Kinesis Julia Hu AWS Machine Learning Blog

​ Introduction to Python operators Understanding the different types of operators and their usage is important for writing efficientand effective Python code. Python operators are special symbols that are used to performspecific operations on values and variables. These operators operate on values called operands. Example:… Read More »Python Operators Explained with Examples sailaja Spark By {Examples}