AWS Machine Learning Blog

APRIL 16, 2024

In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.

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SQL AWS Database Data Scientist 129

Flipboard

DECEMBER 6, 2023

In this post, we discuss a Q&A bot use case that Q4 has implemented, the challenges that numerical and structured datasets presented, and how Q4 concluded that using SQL may be a viable solution. RAG with semantic search – Conventional RAG with semantic search was the last step before moving to SQL generation.

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SQL Database AWS Machine Learning 168

AWS Machine Learning Blog

AUGUST 20, 2024

Natural language is ambiguous and imprecise, whereas data adheres to rigid schemas. For example, SQL queries can be complex and unintuitive for non-technical users. Domain-specific terminology further complicates the mapping process. The following diagram illustrates this workflow.

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SQL AWS Database Natural Language Processing 122

AWS Machine Learning Blog

DECEMBER 29, 2022

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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AWS Machine Learning Machine Learning Database 139

AWS Machine Learning Blog

JUNE 13, 2023

The natural language capabilities allow non-technical users to query data through conversational English rather than complex SQL. The AI and language models must identify the appropriate data sources, generate effective SQL queries, and produce coherent responses with embedded results at scale.

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Database SQL AWS AI 101

AWS Machine Learning Blog

JUNE 25, 2024

Businesses can use LLMs to gain valuable insights, streamline processes, and deliver enhanced customer experiences. In addition, the generative business intelligence (BI) capabilities of QuickSight allow you to ask questions about customer feedback using natural language, without the need to write SQL queries or learn a BI tool.

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AWS Natural Language Processing Machine Learning Machine Learning 128

IBM Journey to AI blog

JULY 17, 2023

This allows users to accomplish different Natural Language Processing (NLP) functional tasks and take advantage of IBM vetted pre-trained open-source foundation models. Encoder-decoder and decoder-only large language models are available in the Prompt Lab today. To bridge the tuning gap, watsonx.ai

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AI AI Machine Learning Machine Learning 74