Tue.Sep 20, 2022

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More Performance Evaluation Metrics for Classification Problems You Should Know

KDnuggets

When building and optimizing your classification model, measuring how accurately it predicts your expected outcome is crucial. However, this metric alone is never the entire story, as it can still offer misleading results. That's where these additional performance evaluations come into play to help tease out more meaning from your model.

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Keeping Multiple Databases in Sync Using Kafka Connect and CDC

Confluent

Microservices have numerous benefits, but data silos are incredibly challenging. Learn how Kafka Connect and CDC provide real-time database synchronization, bridging data silos between all microservice applications.

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How To Calculate Algorithm Efficiency

KDnuggets

In this article, we will discuss how to calculate algorithm efficiency, focusing on two main ways to measure it and providing an overview of the calculation process.

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Ethics Sheet for AI-assisted Comic Book Art Generation

Cloudera

Introduction. This blog is intended to serve as an ethics sheet for the task of AI-assisted comic book art generation, inspired by “ Ethics Sheets for AI Tasks.” AI-assisted comic book art generation is a task I proposed in a blog post I authored on behalf of my employer, Cloudera. I’m a research engineer by trade and have been involved in software creation in some way or another for most of my professional life.

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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Top Posts September 12-18: How to Select Rows and Columns in Pandas

KDnuggets

How to Select Rows and Columns in Pandas Using [ ],loc, iloc,at and.iat • Free Python for Data Science Course • 5 Data Science Skills That Pay & 5 That Don't • 7 Data Analytics Interview Questions & Answers • 5 Tricky SQL Queries Solved.

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3 Use Cases for Real-Time Blockchain Analytics

Rockset

Introduction Cryptocurrencies and NFTs have helped bring blockchain technology to the mainstream over the last few years, driven by the potential for astronomic financial returns. As more users become familiar with blockchain, attention and resources have started to shift towards other use cases for decentralized applications, or dApps. dApps are built on blockchains and are the use case layer for web3 infrastructure, offering a wide range of services.

More Trending

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How Can Real-Time Customer Analytics Lead To More Optimized and Refined Customer Experiences?

Striim

Modern-day customers have higher expectations from the brands they interact with. They crave customer experiences that are more timely, targeted, and personalized to their needs. Brands can meet these expectations by integrating real-time analytics into their customer experience. According to a study from Harvard Business Review, 44% of organizations found the adoption of real-time customer analytics to increase their total number of customers and revenue.

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Learn How Different Data Visualizations Work

KDnuggets

Data Literacy Month at DataCamp is in full swing. DataCamp’s three-part series on demystifying data visualizations explores how to capture trends, demonstrate relationships, and explore distributions. Start learning today.

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Big Data (Quality), Small Data Team: How Prefect Saved 20 Hours Per Week with Data Observability

Monte Carlo

Data teams spend millions per year tackling the persistent challenges of data downtime. However, it’s often the leanest data teams that feel the sting of poor data quality the most. Here’s how Prefect , Series B startup and creator of the popular data orchestration tool, harnessed the power of data observability to preserve headcount, improve data quality and reduce time to detection and resolution for data incidents.

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What Is Product Planning: Process, Objectives, and Significance

U-Next

Introduction to the Product Planning Process . Product Management has seen a lot of advancement in recent years. The number of searches for “ Product Management ” on Google has grown by over 50% in the past five years. This is largely due to the increased focus on customer experience and the need to constantly innovate to stay ahead of the competition.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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Data-Driven Change: Essential Mindsets

Elder Research

The post Data-Driven Change: Essential Mindsets appeared first on Elder Research.

Data 52
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Unit testing in Apache Hop - complete, correct and consistent data

know.bi

What is data testing, and why should you test your data? Apache Hop is a data engineering and data orchestration platform that allows data engineers and data developers to visually design workflows and data pipelines to build robust solutions. However, building data pipelines is just the start. You want to run your workflows and pipelines in production reliably, and you want to make sure your data is processed exactly the way you want it to.

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MLOps Principles to build Picnic’s Data Science Platform

Picnic Engineering

Here at Picnic, we love data. Over the last years, Picnic has grown into a data-driven online supermarket that is active in three countries. By leveraging data and algorithms, we have been able to support the company’s growth while maintaining high service levels. Besides numerous demand forecasting models, we have for example built machine learning models to improve our customer service and increase the efficiency of our trips.