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Harnessing Machine Learning on Big Data with PySpark on AWS

ODSC - Open Data Science

Be sure to check out his talk, “ Build Classification and Regression Models with Spark on AWS ,” there! In the unceasingly dynamic arena of data science, discerning and applying the right instruments can significantly shape the outcomes of your machine learning initiatives. A cordial greeting to all data science enthusiasts!

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Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

Flipboard

Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics.

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Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

AWS Machine Learning Blog

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.

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Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

We explain the metrics and show techniques to deal with data to obtain better model performance. Prerequisites If you would like to implement all or some of the tasks described in this post, you need an AWS account with access to SageMaker Canvas. Let’s try to improve the model performance using a data-centric approach.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Data Preparation for AI Projects Data preparation is critical in any AI project, laying the foundation for accurate and reliable model outcomes. This section explores the essential steps in preparing data for AI applications, emphasising data quality’s active role in achieving successful AI models.

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

There is a position called Data Analyst whose work is to analyze the historical data, and from that, they will derive some KPI s (Key Performance Indicators) for making any further calls. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.

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Large Language Models: A Complete Guide

Heartbeat

In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning. We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods.