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Evaluation Metrics With Python Codes

Analytics Vidhya

Introduction The basic idea of building a machine learning model is to assess the relationship between the dependent and independent variables. In doing so, we need to optimize the model performance. The post Evaluation Metrics With Python Codes appeared first on Analytics Vidhya.

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Managing risk in machine learning

O'Reilly on Data

As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. We recently conducted a survey which garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. Let’s begin by looking at the state of adoption.

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Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data).

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Specialized tools for machine learning development and model governance are becoming essential

O'Reilly on Data

Why companies are turning to specialized machine learning tools like MLflow. A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. Image by Matei Zaharia; used with permission.

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What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

Data is typically organized into project-specific schemas optimized for business intelligence (BI) applications, advanced analytics, and machine learning. Similarly, downstream business metrics in the Gold layer may appear skewed due to missing segments, which can impact high-stakes decisions.

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The future of Gen AI in analytics

CIO Business Intelligence

As businesses increasingly rely on digital platforms to interact with customers, the need for advanced tools to understand and optimize these experiences has never been greater. While Felix AI already enables businesses to process data at scale and act on insights faster, the potential for further automation and optimization is vast.

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