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From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

AWS Machine Learning Blog

For instance, analyzing large tables might require prompting the LLM to generate Python or SQL and running it, rather than passing the tabular data to the LLM. The available data sources are: Stock Prices Database Contains historical stock price data for publicly traded companies. I need to see the latest 10-K filing for Amazon.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

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Use language embeddings for zero-shot classification and semantic search with Amazon Bedrock

AWS Machine Learning Blog

Caching is performed on Amazon CloudFront for certain topics to ease the database load. Amazon Aurora PostgreSQL-Compatible Edition and pgvector Amazon Aurora PostgreSQL-Compatible is used as the database, both for the functionality of the application itself and as a vector store using pgvector. Its hosted on AWS Lambda.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

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[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

The K-Nearest Neighbor Algorithm is a good example of an algorithm with low bias and high variance. This trade-off can easily be reversed by increasing the k value which in turn results in increasing the number of neighbours. Is Python and SQL enough for data science? Let us see some examples.