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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning Blog

This historical data will allow the function to analyze sales trends, product performance, and other relevant metrics over this seven-year period. Prompt 2: Were there any major world events in 2016 affecting the sale of Vegetables? In your Amazon Bedrock IDE Chat agent application, expand the Functions section on the screen.

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Speaker Spotlight: Q&A With TU Wien’s Allan Hanbury – Data Natives Berlin 2016

Dataconomy

The post Speaker Spotlight: Q&A With TU Wien’s Allan Hanbury – Data Natives Berlin 2016 appeared first on Dataconomy. This led to the recent founding of a start-up, ContextFlow, which is bringing the.

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Introduction to Data Science: How to “Big Data” with Python

Dataconomy

Katharine Jarmul and Data Natives are joining forces to give you an amazing chance to delve deeply into Python and how to apply it to data manipulation, and data wrangling. By the end of her workshop, Learn Python for Data Analysis, you will feel comfortable importing and running simple Python analysis on your.

Big Data 196
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What is the metalog distribution?

SAS Software

The metalog family of distributions (Keelin, Decision Analysis, 2016) is a flexible family that can model a wide range of continuous univariate data distributions when the data-generating mechanism is unknown. This article provides an overview of the metalog distributions. The post What is the metalog distribution?

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Use the metalog distribution in SAS

SAS Software

A previous article describes the metalog distribution (Keelin, 2016). The metalog distribution is a flexible family of distributions that can model a wide range of shapes for data distributions. The metalog system can model bounded, semibounded, and unbounded continuous distributions.

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How to Ensure AI Models Reflect the Richness of Human Diversity

Towards AI

I am fascinated by websites like fivethirtyeight.com, — I spent hours glued to their polling and predictive statistics leading up to the 2016 and 2020 US elections (boy, they sure got it wrong in 2016, eh?). Isn’t AI just great for this sort of analysis?

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Data Analysis at Warp Speed: Explore the World of Polars

Mlearning.ai

Empowering Data Scientists and Engineers with Lightning-Fast Data Analysis and Transformation Capabilities Photo by Hans-Jurgen Mager on Unsplash ?Goal ⏱️Performance benchmarking Let’s try it on Kaggle competition dataset based on the 2016 NYC Yellow Cab trip record data and see the numbers using different libraries.