Analytics Vidhya

JANUARY 27, 2022

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. There are two types of ML models, classification and regression; for each ML […].

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Analytics Vidhya

FEBRUARY 16, 2023

Introduction Partial AUC (Area Under the Curve) scores are a valuable tool for evaluating the performance of binary classification models, particularly when the class distribution is highly imbalanced.

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Metrics Modeling Analytics Statistics 276

Analytics Vidhya

MAY 19, 2021

The post Know The Best Evaluation Metrics for Your Regression Model ! ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Machine Learning is a branch of Artificial Intelligence. It contains. appeared first on Analytics Vidhya.

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Analytics Vidhya

MAY 21, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Model Building in Machine Learning is an important component of. The post Importance of Cross Validation: Are Evaluation Metrics enough? appeared first on Analytics Vidhya.

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Analytics Vidhya

JANUARY 28, 2022

The Model development process undergoes multiple iterations and finally, a model which has acceptable performance metrics on test data is taken to the production […]. The post Deploying ML Models Using Kubernetes appeared first on Analytics Vidhya.

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David Menninger's Analyst Perspectives

DECEMBER 23, 2022

Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Organizations can also do a better job tying AnalyticOps to DataOps and do more to define and manage metrics. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes.

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DataKitchen

MARCH 12, 2024

Key Success Metrics, Benefits, and Results for Data Observability Using DataKitchen Software Lowering Serious Production Errors Key Benefit Errors in production can come from many sources – poor data, problems in the production process, being late, or infrastructure problems. Data errors can cause compliance risks.

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Analytics Vidhya

OCTOBER 12, 2020

Introduction Machine learning is about building a predictive model using historical data. The post Quick Guide to Evaluation Metrics for Supervised and Unsupervised Machine Learning appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon.

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Dataiku

DECEMBER 5, 2023

Ever wondered if your model could improve from a performance score of.38 At Everyday AI NYC 2023 , Emma Huang and Cassie Chuljian, senior data scientists at Dataiku, unraveled the secrets behind optimizing model metrics. This blog will go over the seven-step journey they presented to help elevate your data science game.

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datapine

OCTOBER 7, 2020

By establishing clear operational metrics and evaluate performance, companies have the advantage of using what is crucial to stay competitive in the market, and that’s data. Your Chance: Want to visualize & track operational metrics with ease? What Are Metrics And Why Are They Important?

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Domino Data Lab

MARCH 3, 2021

It’s often stated that nothing changes inside an enterprise because you’ve built a model. In some cases, data science does generate models directly to revenue, such as a contextual deal engine that targets people with offers that they can instantly redeem. But what about good decisions?

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KDnuggets

SEPTEMBER 20, 2022

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.

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CIO Business Intelligence

MARCH 6, 2024

What has IT’s role been in the transformation to a SaaS model? We built that end-to-end data model and process from scratch while we ran the old business. We knew we had a unique opportunity to build a new end-to-end architecture with a common AI-powered data model. Look at changing metrics and KPIs as a gift.

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DataKitchen

MARCH 16, 2023

But wait, she asks you for your team metrics. Where is your metrics report? What are the metrics that matter? Gartner attempted to list every metric under the sun in their recent report , “T oolkit: Delivery Metrics for DataOps, Self-Service Analytics, ModelOps, and MLOps, ” published February 7, 2023.

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datapine

AUGUST 14, 2020

A customer retention dashboard and metrics depicted in a neat visual will help you in monitoring, analyzing, and managing multiple customer-centric points and how they echo in your business. But first, let’s start with a basic definition. Your Chance: Want to build a dashboard for customer retention?

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Dashboards Metrics B2B Visualization 176

Analytics Vidhya

FEBRUARY 4, 2020

The post How to Create a Test Set to Approximate Business Metrics Offline appeared first on Analytics Vidhya. Introduction Most Kaggle-like machine learning hackathons miss a core aspect of a machine learning workflow – preparing an offline evaluation environment while building an.

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datapine

SEPTEMBER 29, 2022

6) Data Quality Metrics Examples. Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. These needs are then quantified into data models for acquisition and delivery. Table of Contents. 1) What Is Data Quality Management?

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Analytics Vidhya

MARCH 7, 2024

While ChatGPT, developed by OpenAI, stands as a titan in conversational AI, “Perplexity” pertains more to a performance metric used in evaluating language models.

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Smart Data Collective

FEBRUARY 22, 2022

More companies than ever are shifting towards digital business models. They have access to troves of valuable data, which can be used to improve the profitability of their business models. Keep reading to learn more about the metrics that data-driven online stores are prioritizing.

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Analytics Vidhya

DECEMBER 2, 2021

Introduction to Evaluation of Classification Model As the topic suggests we are going to study Classification model evaluation. The post Evaluating A Classification Model for Data Science appeared first on Analytics Vidhya. Before starting out directly with classification let’s talk about ML tasks in general.

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Jedox

JULY 6, 2021

When considering the performance of any forecasting model, the prediction values it produces must be evaluated. This is done by calculating suitable error metrics. An error metric is a way to quantify the performance of a model and provides a way for the forecaster to quantitatively compare different models 1.

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CIO Business Intelligence

MARCH 25, 2024

Modern digital organisations tend to use an agile approach to delivery, with cross-functional teams, product-based operating models , and persistent funding. But to deliver transformative initiatives, CIOs need to embrace the agile, product-based approach, and that means convincing the CFO to switch to a persistent funding model.

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Modeling Cost-Benefit IT ROI 124

Analytics Vidhya

OCTOBER 28, 2021

This article was published as a part of the Data Science Blogathon The objective of any machine learning model is to understand and learn patterns from the data which can further be used to make predictions or answer questions or simply just understand the underlying pattern that is otherwise not evident candidly. Most of the […].

Modeling 282

Modeling Machine Learning Data Science Publishing 282

Smart Data Collective

SEPTEMBER 21, 2021

A growing number of businesses are starting to look for new data-driven approaches to streamline their business models. However, your data-driven business model won’t be very helpful if you don’t focus on the right metrics. Targeting the Right Variables for Your Data-Driven Retail Business Model.

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Cloudera

JULY 21, 2022

Cloudera has partnered with Rill Data, an expert in metrics at any scale, as Cloudera’s preferred ISV partner to provide technical expertise and support services for Apache Druid customers. Deploying metrics shouldn’t be so hard. Rill solves this with pipeline services and Rill Developer, a free SQL-based data modeler.

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Metrics Slice and Dice Data Warehouse Dashboards 84

CIO Business Intelligence

JANUARY 18, 2024

Recognizing this need, we have created a cutting-edge VSM maturity model. Drawing upon our extensive experience facilitating successful VSM initiatives within large-scale enterprises, this model serves as a compass, offering insights into their current VSM maturity and providing practical direction to advance and reap the additional benefits.

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KDnuggets

OCTOBER 10, 2023

Navigating the Maze of Accuracy, Precision, and Recall in Machine Learning.

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Smart Data Collective

AUGUST 16, 2021

Analytics data can be very useful for companies trying to create profitable online business models. Pay attention to the following metrics in your analytics dashboard to help you achieve greater success with your store. This metric is the average number you have to put in to get new customers. Audience Information.

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Metrics Analytics Cost-Benefit Sales 129

AWS Big Data

JUNE 27, 2023

Our previous solution offered visualization of key metrics, but point-in-time snapshots produced only in PDF format. In this post, we discuss how we built a solution using QuickSight that delivers real-time visibility of key metrics to public sector recruiters.

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Cloudera

NOVEMBER 29, 2023

That’s why we’re excited to announce the Cloudera Model Registry as generally available, a game-changer that’s set to transform the way you manage your machine learning models in production environments. That’s where the Model Registry steps in. That’s where the Model Registry steps in.

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Modeling Machine Learning Metadata Metrics 83

Domino Data Lab

OCTOBER 16, 2019

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability, consistency and performance in the future. Machine learning models: running wild. They may also experience relief when the model “works” in production.

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IBM Big Data Hub

JANUARY 3, 2024

Most everyone has heard of large language models, or LLMs, since generative AI has entered our daily lexicon through its amazing text and image generating capabilities, and its promise as a revolution in how enterprises handle core business functions. Enter the world of Large Speech Models, or LSMs.

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Analytics Vidhya

NOVEMBER 25, 2022

Source:pixabay.com Introduction State-of-the-art machine learning models and artificially intelligent machines are made of complex processes like adjusting hyperparameters and choosing models that provide better accuracy and the metrics that govern this behavior.

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Analytics Vidhya

DECEMBER 16, 2020

The post Decluttering the performance measures of classification models appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Introduction There are so many performance evaluation measures when it comes to.

Measurement 295

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KDnuggets

JANUARY 12, 2022

Using one or two performance metrics seems sufficient to claim that your ML model is good — chances are that it’s not.

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Machine Learning Modeling Metrics 160

DataRobot Blog

JUNE 27, 2022

In our previous two posts, we discussed extensively how modelers are able to both develop and validate machine learning models while following the guidelines outlined by the Federal Reserve Board (FRB) in SR 11-7. Monitoring Model Metrics.

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