Clustering, Computer Science and Data Preparation

PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

AWS Machine Learning Blog

DECEMBER 24, 2024

The process of setting up and configuring a distributed training environment can be complex, requiring expertise in server management, cluster configuration, networking and distributed computing. Its mounted at /fsx on the head and compute nodes. Scheduler : SLURM is used as the job scheduler for the cluster.

AWS

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Credit Card Fraud Detection Using Spectral Clustering

PyImageSearch

SEPTEMBER 16, 2024

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.

Clustering

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Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning Blog

SEPTEMBER 18, 2023

With Ray and AIR, the same Python code can scale seamlessly from a laptop to a large cluster. The managed infrastructure of SageMaker and features like processing jobs, training jobs, and hyperparameter tuning jobs can use Ray libraries underneath for distributed computing. You can specify resource requirements in actors too.

Machine Learning

Machine Learning Machine Learning ML ML

Webinars

How to Achieve High-Accuracy Results When Using LLMs

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

AUGUST 17, 2023

Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster. In the processing job API, provide this path to the parameter of submit_jars to the node of the Spark cluster that the processing job creates. We attached the IAM role to the Redshift cluster that we created earlier.

ML

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

JULY 12, 2024

Natural Language Processing (NLP) This is a field of computer science that deals with the interaction between computers and human language. Computer Vision This is a field of computer science that deals with the extraction of information from images and videos. Why is Data Preparation Crucial in AI Projects?

Artificial Intelligence

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Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning Blog

AUGUST 22, 2023

Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed. Data is split into a training dataset and a testing dataset. Details of the data preparation code are in the following notebook.

Machine Learning

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How LLMs are Transforming Bot Building, Botnet Detection at Scale, and Declarative ML for Engineers

ODSC - Open Data Science

APRIL 13, 2023

5 Industries Using Synthetic Data in Practice Here’s an overview of what synthetic data is and a few examples of how various industries have benefited from it. Hands-on Data-Centric AI: Data Preparation Tuning — Why and How? Here’s how.

ML

ML ML Data Science Machine Learning

Predictive Maintenance Using Isolation Forest

PyImageSearch

OCTOBER 21, 2024

In the first part of our Anomaly Detection 101 series, we learned the fundamentals of Anomaly Detection and saw how spectral clustering can be used for credit card fraud detection. This method helps in identifying fraudulent transactions by grouping similar data points and detecting outliers. Or requires a degree in computer science?

Algorithm

Algorithm Deep Learning Deep Learning Data Preparation

“Fall in love with your data”—Snorkel AI’s Enterprise LLM Summit

Snorkel AI

JANUARY 26, 2024

Data scientists can best improve LLM performance on specific tasks by feeding them the right data prepared in the right way. Snorkel engineers and researchers, he noted, used scalable data development tools to improve many parts of this system, including their embedding and retrieval models. Slides for this session.

Data Science

Data Science AI AI Machine Learning

Understanding Everything About UCI Machine Learning Repository!

Pickl AI

DECEMBER 3, 2024

It is a central hub for researchers, data scientists, and Machine Learning practitioners to access real-world data crucial for building, testing, and refining Machine Learning models. The publicly available repository offers datasets for various tasks, including classification, regression, clustering, and more.

Machine Learning

Machine Learning Machine Learning Clustering Supervised Learning

How Data Science and AI is Changing the Future

Pickl AI

NOVEMBER 5, 2024

Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines various techniques from statistics, mathematics, computer science, and domain expertise to interpret complex data sets.

Data Science

Data Science Artificial Intelligence Artificial Intelligence Machine Learning

“Fall in love with your data”—Snorkel AI’s Enterprise LLM Summit

Snorkel AI

JANUARY 26, 2024

Data scientists can best improve LLM performance on specific tasks by feeding them the right data prepared in the right way. Snorkel engineers and researchers, he noted, used scalable data development tools to improve many parts of this system, including their embedding and retrieval models. Slides for this session.

Data Science

Data Science Data Scientist AI AI

Understanding Data Science and Data Analysis Life Cycle

Pickl AI

MAY 30, 2024

Understanding Data Science Data Science involves analysing and interpreting complex data sets to uncover valuable insights that can inform decision-making and solve real-world problems. Verify that the data is accurate, complete, and up-to-date. High-quality data is the foundation of reliable analysis.

Data Analysis

Data Analysis Data Analysis Data Science Exploratory Data Analysis

A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

SEPTEMBER 11, 2024

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.

AWS

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Build a Network Intrusion Detection System with Variational Autoencoders

PyImageSearch

NOVEMBER 18, 2024

We will start by setting up libraries and data preparation. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or requires a degree in computer science? intrusions or attacks) and “good” normal connections. That’s not the case.

Deep Learning

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PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

Credit Card Fraud Detection Using Spectral Clustering

Webinars

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Orchestrate Ray-based machine learning workflows using Amazon SageMaker

Webinars

Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Artificial Intelligence Using Python: A Comprehensive Guide

Machine learning with decentralized training data using federated learning on Amazon SageMaker

How LLMs are Transforming Bot Building, Botnet Detection at Scale, and Declarative ML for Engineers

Predictive Maintenance Using Isolation Forest

“Fall in love with your data”—Snorkel AI’s Enterprise LLM Summit

Understanding Everything About UCI Machine Learning Repository!

How Data Science and AI is Changing the Future

“Fall in love with your data”—Snorkel AI’s Enterprise LLM Summit

Understanding Data Science and Data Analysis Life Cycle

A review of purpose-built accelerators for financial services

Build a Network Intrusion Detection System with Variational Autoencoders

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