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Machine learning algorithms

Dataconomy

Machine learning algorithms represent a transformative leap in technology, fundamentally changing how data is analyzed and utilized across various industries. What are machine learning algorithms? Regression: Focuses on predicting continuous values, such as forecasting sales or estimating property prices.

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Training-serving skew

Dataconomy

Training-serving skew is a significant concern in the machine learning domain, affecting the reliability of models in practical applications. Understanding how discrepancies between training data and operational data can impact model performance is essential for developing robust systems. What is training-serving skew?

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Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning Blog

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Solution overview This post focuses on the benefits of using Ray and SageMaker together.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Flipboard

Knowledge base – You need a knowledge base created in Amazon Bedrock with ingested data and metadata. For detailed instructions on setting up a knowledge base, including data preparation, metadata creation, and step-by-step guidance, refer to Amazon Bedrock Knowledge Bases now supports metadata filtering to improve retrieval accuracy.

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Serverless Machine Learning in AWS: Lambda + Step Functions Guide

How to Learn Machine Learning

In this article we will speak about Serverless Machine learning in AWS, so sit back, relax, and enjoy! Introduction to Serverless Machine Learning in AWS Serverless computing reshapes machine learning (ML) workflow deployment through its combination of scalability and low operational cost, and reduced total maintenance expenses.

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Predictive modeling

Dataconomy

Predictive modeling plays a crucial role in transforming vast amounts of data into actionable insights, paving the way for improved decision-making across industries. By leveraging statistical techniques and machine learning, organizations can forecast future trends based on historical data.

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The Ultimate Guide to Data Preparation for Machine Learning

DagsHub

Introduction Machine learning models learn patterns from data and leverage the learning, captured in the model weights, to make predictions on new, unseen data. Data, is therefore, essential to the quality and performance of machine learning models.