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How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model

AWS Machine Learning Blog

This strategic decision was driven by several factors: Efficient data preparation Building a high-quality pre-training dataset is a complex task, involving assembling and preprocessing text data from various sources, including web sources and partner companies. The team opted for fine-tuning on AWS.

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Data science revolution 101 – Unleashing the power of data in the digital age

Data Science Dojo

The primary aim is to make sense of the vast amounts of data generated daily by combining statistical analysis, programming, and data visualization. It is divided into three primary areas: data preparation, data modeling, and data visualization.

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Credit Card Fraud Detection Using Spectral Clustering

PyImageSearch

Home Table of Contents Credit Card Fraud Detection Using Spectral Clustering Understanding Anomaly Detection: Concepts, Types and Algorithms What Is Anomaly Detection? By leveraging anomaly detection, we can uncover hidden irregularities in transaction data that may indicate fraudulent behavior.

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Perform generative AI-powered data prep and no-code ML over any size of data using Amazon SageMaker Canvas

AWS Machine Learning Blog

You need data engineering expertise and time to develop the proper scripts and pipelines to wrangle, clean, and transform data. Afterward, you need to manage complex clusters to process and train your ML models over these large-scale datasets. These features can find temporal patterns in the data that can influence the baseFare.

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Supervised vs Unsupervised Learning: Key Differences

How to Learn Machine Learning

It groups similar data points or identifies outliers without prior guidance. Type of Data Used in Each Approach Supervised learning depends on data that has been organized and labeled. This data preparation process ensures that every example in the dataset has an input and a known output.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

Learning means identifying and capturing historical patterns from the data, and inference means mapping a current value to the historical pattern. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference.

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Serverless Machine Learning in AWS: Lambda + Step Functions Guide

How to Learn Machine Learning

We all know the management of Machine Learning systems can be complex: it typically involves the operation of servers, containers, and Kubernetes clusters, which requires prolonged processes and expertise in systems management. For example, services like S3, API Gateway, and Kinesis can trigger processes as soon as new data is detected.