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Data mining

Dataconomy

Data mining is a fascinating field that blends statistical techniques, machine learning, and database systems to reveal insights hidden within vast amounts of data. Businesses across various sectors are leveraging data mining to gain a competitive edge, improve decision-making, and optimize operations.

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LLM app platforms

Dataconomy

Definition and functionality of LLM app platforms These platforms encompass various capabilities specifically tailored for LLM development. Data cleaning and annotation Data cleaning: Involves standardizing text and eliminating any unnecessary formatting. KLU.ai: Offers no-code solutions for smooth data source integration.

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Implementing Approximate Nearest Neighbor Search with KD-Trees

PyImageSearch

Or think about a real-time facial recognition system that must match a face in a crowd to a database of thousands. These scenarios demand efficient algorithms to process and retrieve relevant data swiftly. Imagine a database with billions of samples ( ) (e.g., So, how can we perform efficient searches in such big databases?

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Flipboard

Knowledge base – You need a knowledge base created in Amazon Bedrock with ingested data and metadata. For detailed instructions on setting up a knowledge base, including data preparation, metadata creation, and step-by-step guidance, refer to Amazon Bedrock Knowledge Bases now supports metadata filtering to improve retrieval accuracy.

AWS 150
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Amazon Bedrock Model Distillation: Boost function calling accuracy while reducing cost and latency

AWS Machine Learning Blog

In this post, we highlight the advanced data augmentation techniques and performance improvements in Amazon Bedrock Model Distillation with Metas Llama model family. Preparing your data Effective data preparation is crucial for successful distillation of agent function calling capabilities.

AWS 120
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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

Solution overview With SageMaker Studio JupyterLab notebook’s SQL integration, you can now connect to popular data sources like Snowflake, Athena, Amazon Redshift, and Amazon DataZone. For example, you can visually explore data sources like databases, tables, and schemas directly from your JupyterLab ecosystem.

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A generative AI prototype with Amazon Bedrock transforms life sciences and the genome analysis process

Flipboard

This post explores deploying a text-to-SQL pipeline using generative AI models and Amazon Bedrock to ask natural language questions to a genomics database. Text-to-SQL for genomics data Text-to-SQL is a task in natural language processing (NLP) to automatically convert natural language text into SQL queries.

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