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Improving air quality with generative AI

AWS Machine Learning Blog

On December 6 th -8 th 2023, the non-profit organization, Tech to the Rescue , in collaboration with AWS, organized the world’s largest Air Quality Hackathon – aimed at tackling one of the world’s most pressing health and environmental challenges, air pollution. Having a human-in-the-loop to validate each data transformation step is optional.

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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

AWS Machine Learning Blog

Faced with manual dubbing challenges and prohibitive costs, MagellanTV sought out AWS Premier Tier Partner Mission Cloud for an innovative solution. In the backend, AWS Step Functions orchestrates the preceding steps as a pipeline. Each step is run on AWS Lambda or AWS Batch. She received her Ph.D. After earning his Ph.D.

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Object-centric Process Mining on Data Mesh Architectures

Data Science Blog

The creation of this data model requires the data connection to the source system (e.g. SAP ERP), the extraction of the data and, above all, the data modeling for the event log. I probably developed my first object-centric event log back in 2016 and used it for an industrial customer. Click to enlarge!

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Why Open Table Format Architecture is Essential for Modern Data Systems

phData

Data Versioning and Time Travel Open Table Formats empower users with time travel capabilities, allowing them to access previous dataset versions. The first insert statement loads data having c_custkey between 30001 and 40000 – INSERT INTO ib_customers2 SELECT *, '11111111111111' AS HASHKEY FROM snowflake_sample_data.tpch_sf1.customer

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Data Analysis at Warp Speed: Explore the World of Polars

Mlearning.ai

Goal The objective of this post is to demonstrate how Polars performance is much better than other open-source libraries in a variety of data analysis tasks, such as data cleaning, data wrangling, and data visualization. ? It is available in multiple languages: Python, Rust, and NodeJS. pip install modin # modin==0.22.2

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Best Practices for Managing Computer Vision Projects

DagsHub

This means the variety, frequency, and characteristics of examples in the training data should closely match what the model will encounter when deployed. Gather enough data for training, validation, and testing of the models. This is where tools like Dagshub Data Engine come into play.