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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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Measure performance of AWS Glue Data Quality for ETL pipelines

AWS Big Data

AWS Glue Data Quality is built on DeeQu , an open source tool developed and used at Amazon to calculate data quality metrics and verify data quality constraints and changes in the data distribution so you can focus on describing how data should look instead of implementing algorithms. In the Create job section, choose Visual ETL.x

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Introducing Terraform support for Amazon OpenSearch Ingestion

AWS Big Data

OpenSearch Ingestion is a fully managed, serverless data collector that delivers real-time log, metric, and trace data to Amazon OpenSearch Service domains and Amazon OpenSearch Serverless collections. Terraform is an infrastructure as code (IaC) tool that helps you build, deploy, and manage cloud resources efficiently. touch main.tf

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Debunking observability myths – Part 3: Why observability works in every environment, not just large-scale systems

IBM Big Data Hub

For many companies, however, their applications are their business, and the lack of an observability framework can limit their ability to diagnose issues and optimize systems in a timely manner. Even a simple web application can benefit from observability by implementing basic logging and metrics.

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Best practices to implement near-real-time analytics using Amazon Redshift Streaming Ingestion with Amazon MSK

AWS Big Data

Make sure that data is flowing into your MSK topic using Amazon CloudWatch metrics (for example, BytesOutPerSec ). For optimized performance of the streaming materialized view and to reduce storage usage, occasionally purge data from the materialized view using delete , truncate , or alter table append.

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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

from sklearn import metrics. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set." Fraudulent transactions are 0.17% of the test set. 2f%% of the test set." Fraudulent transactions are 50.00% of the test set. Feature Engineering.

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Hitting the Gym With Neural Networks: Implementing a CNN to Classify Gym Equipment

Insight

CNNs have been widely considered state-of-the-art tools for computer vision since 2012, when AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Choosing your loss function and optimizer Finally, in the last block of code, we must compile the model that we just built. Does anything look fishy to you…?

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