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Enrich your serverless data lake with Amazon Bedrock

AWS Big Data

Solution overview The AWS Serverless Data Analytics Pipeline reference architecture provides a comprehensive, serverless solution for ingesting, processing, and analyzing data. We recommend testing your use case and data with different models. Amazon Bedrock has a lot of models to offer, each with their own strengths.

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Designing a SemTech Proof-of-Concept: Get Ready for Our Next Live Online Training

Ontotext

The training is structured to follow the steps of building a simple prototype to test the feasibility of the technology with hands-on guidance by experienced instructors. Still, newcomers are advised to dedicate some time to any of the excellent SPARQL tutorials out there, some of which are referred to in the FAQ section of the training page.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

This post considers a common design for an OCE where a user may be randomly assigned an arm on their first visit during the experiment, with assignment weights referring to the proportion that are randomly assigned to each arm. There are two common reasons assignment weights may change during an OCE.

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Performing Non-Compartmental Analysis with Julia and Pumas AI

Domino Data Lab

Once all packages have been imported, we can move on to loading our test data. We can then proceed with pharmacokinetic modeling, testing the goodness of fit of various models. References. [1] Note that the import may take a while due to the nature of the just-ahead-of-time (JAOT) compiler that Julia uses. Methods Mol Biol.

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Designing a SemTech Proof-of-Concept: Get Ready for Our Next Live Online Training

Ontotext

The training is structured to follow the steps of building a simple prototype to test the feasibility of the technology with hands-on guidance by experienced instructors. Still, newcomers are advised to dedicate some time to any of the excellent SPARQL tutorials out there, some of which are referred to in the FAQ section of the training page.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Their tests are performed using C4.5-generated note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). References. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Chawla et al.,