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Improving air quality with generative AI

AWS Machine Learning Blog

This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. She holds 30+ patents and has co-authored 100+ journal/conference papers.

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Unpacking and Utilizing Vertex with Google Earth Engine for Machine Learning.

Towards AI

Established by Google in 2010, it possesses a vast assortment of geospatial data containing of petabytes of data collected by multiple satellites, such as Sentinel, MODIS, Landsat, and more for analysis. What is Google Earth Engine?

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10 Best CRM Software Platforms

Unite.AI

Pipedrive Pipedrive stands out in the cloud-based CRM space, having rapidly gained traction since its inception in 2010. Additionally, Salesforce provides granular control over page access and field editing, catering to different employee roles and maintaining data integrity and security.

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Discovering Major Differences between SQL and MySQL

Pickl AI

This consistency makes SQL a primary choice for data-driven applications, including business intelligence, analytics, and web development. You can create tables and define their relationships with primary and foreign keys, ensuring data integrity and accuracy.

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What if LLM is the ultimate data janitor

Bugra Akyildiz

A data janitor is a person who works to take big data and condense it into useful amounts of information. Also known as a "data wrangler", a data janitor sifts through data for companies in the information technology industry. Usual programming will continue next week as usual.

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A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

Data mining: concepts and techniques. MapReduce: simplified data processing on large clusters. Data integration and ETL: techniques for data management. Subscribe to the newsletter to not miss my latest posts: Subscribe Now ? Connect with me on LinkedIn for updates. References Han, J., Kamber, M., & Pei, J.

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Flipboard

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. By analyzing millions of metadata elements and data flows, Iris could make intelligent suggestions to users, democratizing data integration and allowing even those without a deep technical background to create complex workflows.

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