Generative Data Intelligence

Tag: AWS Glue

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

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process...

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Build a robust text-to-SQL solution generating complex queries, self-correcting, and querying diverse data sources | Amazon Web Services

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL...

How Booking.com modernized its ML experimentation framework with Amazon SageMaker | Amazon Web Services

This post is co-written with Kostia Kofman and Jenny Tokar from Booking.com. As a global leader in...

Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart | Amazon Web Services

One of the most useful application patterns for generative AI workloads is Retrieval Augmented Generation (RAG). In the RAG pattern, we find pieces of...

Use mobility data to derive insights using Amazon SageMaker geospatial capabilities | Amazon Web Services

Geospatial data is data about specific locations on the earth’s surface. It can represent a geographical area as a whole or it can represent...

REPLY: Storm Reply ayuda a EUROGATE Group a analizar datos de máquinas y sistemas con el acelerador Storm Innovator

TURÍN, Italia–(BUSINESS WIRE)–Storm Reply, especialista en servicios profesionales de computación en la nube, ha desarrollado una innovadora solución de IoT en la nube para...

Modernizing data science lifecycle management with AWS and Wipro | Amazon Web Services

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination...

Generating value from enterprise data: Best practices for Text2SQL and generative AI | Amazon Web Services

Generative AI has opened up a lot of potential in the field of AI. We are seeing numerous uses, including text generation, code generation,...

Identify cybersecurity anomalies in your Amazon Security Lake data using Amazon SageMaker | Amazon Web Services

Customers are faced with increasing security threats and vulnerabilities across infrastructure and application resources as their digital footprint has expanded and the business impact...

Streamlining ETL data processing at Talent.com with Amazon SageMaker | Amazon Web Services

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Established in 2011, Talent.com aggregates paid...

Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio | Amazon Web Services

This post is co-written with Marc Neumann, Amor Steinberg and Marinus Krommenhoek from BMW Group. The BMW Group – headquartered in Munich, Germany –...

Optimize for sustainability with Amazon CodeWhisperer | Amazon Web Services

This post explores how Amazon CodeWhisperer can help with code optimization for sustainability through increased resource efficiency. Computationally resource-efficient coding is one technique that...

Visualize an Amazon Comprehend analysis with a word cloud in Amazon QuickSight | Amazon Web Services

Searching for insights in a repository of free-form text documents can be like finding a needle in a haystack. A traditional approach might be...

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