Tableau

JANUARY 27, 2021

In the recent Gartner Peer Insights ‘Voice of the Customer’: Data Preparation Tools report , Tableau is the only vendor recognized in the Gartner Peer Insights Customers’ Choice distinction across all regions, company sizes, and industries—including the sole Customers’ Choice by users in the finance vertical. .

Tableau 102

Tableau Business Intelligence Business Intelligence Analytics 102

AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

ML 86

ML ML Database AWS 86

Tableau

JANUARY 27, 2021

In the recent Gartner Peer Insights ‘Voice of the Customer’: Data Preparation Tools report , Tableau is the only vendor recognized in the Gartner Peer Insights Customers’ Choice distinction across all regions, company sizes, and industries—including the sole Customers’ Choice by users in the finance vertical. .

Tableau 52

Tableau Business Intelligence Business Intelligence Analytics 52

AWS Machine Learning Blog

NOVEMBER 22, 2023

In this code talk, learn how to prepare data at scale using built-in data preparation assistance, co-edit the same notebook in real time, and automate conversion of notebook code to production-ready jobs. Since 2013 he has helped AWS customers adopt AI/ML technology as a Solutions Architect.

AWS 129

AWS ML ML AI 129

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Mlearning.ai

JUNE 21, 2023

We then go over all the project components and processes, from data preparation, model training, and experiment tracking to model evaluation, to equip you with the skills to construct your own emotion recognition model. FER, Facial Expression Recognition, is an open-source dataset released in 2013. What is the FER dataset?

Deep Learning 52

Deep Learning Deep Learning ML ML 52

IBM Journey to AI blog

FEBRUARY 22, 2024

Detailing ethics practices throughout the AI lifecycle, corresponding to business (or mission) goals, data preparation and modeling, evaluation and deployment. In 2013, IBM embarked on the journey of explainability and transparency in AI and machine learning. The CRISP-DM model is useful here.

AI 116

AI AI Artificial Intelligence Artificial Intelligence 116

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