Remove 2024 Remove Clustering Remove Data Preparation
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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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Must-Have Prompt Engineering Skills for 2024

ODSC - Open Data Science

Using skills such as statistical analysis and data visualization techniques, prompt engineers can assess the effectiveness of different prompts and understand patterns in the responses. This skill focuses on minimizing the resources and time required for an LLM to generate output based on your prompts.

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Predictive Maintenance Using Isolation Forest

PyImageSearch

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. detection of potential failures or issues).

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How to Choose MLOps Tools: In-Depth Guide for 2024

DagsHub

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

These environments ranged from individual laptops and desktops to diverse on-premises computational clusters and cloud-based infrastructure. Access to AWS environments SageMaker and associated AI/ML services are accessed with security guardrails for data preparation, model development, training, annotation, and deployment.

AWS 106
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TAI #109: Cost and Capability Leaders Switching Places With GPT-4o Mini and LLama 3.1?

Towards AI

Competition at the leading edge of LLMs is certainly heating up, and it is only getting easier to train LLMs now that large H100 clusters are available at many companies, open datasets are released, and many techniques, best practices, and frameworks have been discovered and released. Why should you care?

AI 105
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Getting Started With Snowflake: Best Practices For Launching

phData

This blog was originally written by Erik Hyrkas and updated for 2024 by Justin Delisi This isn’t meant to be a technical how-to guide — most of those details are readily available via a quick Google search — but rather an opinionated review of key processes and potential approaches. In this case, the max cluster count should also be two.