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Precise Software Solutions implements ML as a service on AWS to save time and money for federal agency

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Precise), an Amazon Web Services (AWS) Partner , participated in the AWS Think Big for Small Business Program (TBSB) to expand their AWS capabilities and to grow their business in the public sector. The platform helped the agency digitize and process forms, pictures, and other documents. Precise Software Solutions, Inc.

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Connecting Amazon Redshift and RStudio on Amazon SageMaker

AWS Machine Learning Blog

Many of the RStudio on SageMaker users are also users of Amazon Redshift , a fully managed, petabyte-scale, massively parallel data warehouse for data storage and analytical workloads. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.

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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

AWS Machine Learning Blog

Businesses can use LLMs to gain valuable insights, streamline processes, and deliver enhanced customer experiences. In the first step, an AWS Lambda function reads and validates the file, and extracts the raw data. The raw data is processed by an LLM using a preconfigured user prompt.

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From concept to reality: Navigating the Journey of RAG from proof of concept to production

AWS Machine Learning Blog

Many organizations store their data in structured formats within data warehouses and data lakes. Amazon Bedrock Knowledge Bases offers a feature that lets you connect your RAG workflow to structured data stores. The following is a sample architecture for a secure and scalable RAG-based web application.

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Unlock the power of structured data for enterprises using natural language with Amazon Q Business

AWS Machine Learning Blog

The inherent ambiguity of natural language can also result in multiple interpretations of a single query, making it difficult to accurately understand the user’s precise intent. To bridge this gap, you need advanced natural language processing (NLP) to map user queries to database schema, tables, and operations.

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Elevate marketing intelligence with Amazon Bedrock and LLMs for content creation, sentiment analysis, and campaign performance evaluation

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Prerequisites This solution requires you to have an AWS account with the appropriate permissions. The LLM processes the textual content and outputs classifications (for example, positive, negative, or neutral) and explanations. By integrating LLMs such as Anthropics Claude 3.5 Sonnet , Amazon Nova Pro , and Meta Llama 3.2

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

IAM role – SageMaker requires an AWS Identity and Access Management (IAM) role to be assigned to a SageMaker Studio domain or user profile to manage permissions effectively. An execution role update may be required to bring in data browsing and the SQL run feature. You need to create AWS Glue connections with specific connection types.

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