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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Modeling Machine Learning Metrics Testing 346

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 […].

Metrics 260

Metrics Machine Learning Data Science Publishing 260

Analytics Vidhya

MARCH 26, 2025

Large language models (LLMs) have become incredibly advanced and widely used, powering everything from chatbots to content creation. With this rise, the need for reliable evaluation metrics has never been greater. How do we keep AI safe and helpful as it grows more central to our digital lives?

Modeling 125

Modeling Metrics Measurement Analytics 125

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.

Metrics 291

Metrics Modeling Analytics Statistics 291

CIO Business Intelligence

NOVEMBER 7, 2024

However, the metrics used to evaluate CIOs are hindering progress. The status of digital transformation Digital transformation is a complex, multiyear journey that involves not only adopting innovative technologies but also rethinking business processes, customer interactions, and revenue models.

Digital Transformation 128

Digital Transformation Metrics Measurement Key Performance Indicator 128

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.

Machine Learning 306

Machine Learning Metrics Predictive Modeling Data Science 306

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.

Metrics 201

Metrics Modeling Machine Learning Data Science 201

Analytics Vidhya

AUGUST 25, 2024

Introduction Assessing a machine learning model isn’t just the final step—it’s the keystone of success. Imagine building a cutting-edge model that dazzles with high accuracy, only to find it crumbles under real-world pressure. appeared first on Analytics Vidhya.

Modeling 290

Modeling Machine Learning Metrics Analytics 290

Analytics Vidhya

MARCH 29, 2025

As Large Language Models (LLMs) blur the lines between human and machine-generated content, the quest for reliable evaluation metrics has become more critical than ever. Imagine an AI that can write poetry, draft legal documents, or summarize complex research papersbut how do we truly measure its effectiveness?

Metrics 199

Metrics Measurement Modeling Analytics 199

Analytics Vidhya

JULY 9, 2024

This powerful metric, called relative entropy or information gain, has become indispensable in various fields, from statistical inference to deep learning. Introduction Few concepts in mathematics and information theory have profoundly impacted modern machine learning and artificial intelligence, such as the Kullback-Leibler (KL) divergence.

Machine Learning 255

Machine Learning Metrics Deep Learning Statistics 255

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?

KPI 269

KPI Metrics Sales Visualization 269

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.

Metrics 290

Metrics Testing Machine Learning Analytics 290

Analytics Vidhya

FEBRUARY 29, 2024

Introduction Language models are usually trained on extensive amounts of textual data. These models aid in generating natural-sounding responses like humans. Additionally, they can perform various language-related tasks such as translation, text summarization, text generation, answering specific questions, and more.

Metrics 162

Metrics Modeling Analytics 162

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.

Data Science 318

Data Science Modeling Machine Learning Publishing 318

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 364

Marketing Experimentation Metrics Testing 364

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 291

Modeling Machine Learning Data Science Publishing 291

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 306

Measurement Modeling Data Science Publishing 306

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.

Metrics 147

Metrics Data Governance Data Processing Management 147

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?

Data Quality 241

Data Quality Metrics Data-driven Management 241

Analytics Vidhya

JULY 5, 2022

Introduction to Confusion Matrix In a situation where we want to make discrete predictions, we often wish to assess the quality of our model beyond simple metrics like the model’s accuracy, especially if we have many classes. This article was published as a part of the Data Science Blogathon.

Metrics 367

Metrics Data Science Publishing Modeling 367

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.

Metrics 160

Metrics Measurement Optimization Modeling 160

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.

Machine Learning 328

Machine Learning Metrics Data Science Publishing 328

Analytics Vidhya

SEPTEMBER 10, 2021

This article was published as a part of the Data Science Blogathon Introduction With ignite, you can write loops to train the network in just a few lines, add standard metrics calculation out of the box, save the model, etc. Well, for those who have moved from TF to PyTorch, we can say that the ignite […].

Testing 356

Testing Metrics Data Science Publishing 356

O'Reilly on Data

NOVEMBER 19, 2024

Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. See the primary sources “ REALM: Retrieval-Augmented Language Model Pre-Training ” by Kelvin Guu, et al., at Facebook—both from 2020.

Unstructured Data 318

Unstructured Data Structured Data Statistics Modeling 318

Analytics Vidhya

OCTOBER 22, 2021

This article was published as a part of the Data Science Blogathon Introduction Working as an ML engineer, it is common to be in situations where you spend hours to build a great model with desired metrics after carrying out multiple iterations and hyperparameter tuning but cannot get back to the same results with the […].

Machine Learning 348

Machine Learning Metrics Data Science Publishing 348

O'Reilly on Data

MARCH 25, 2025

Throughout this article, well explore real-world examples of LLM application development and then consolidate what weve learned into a set of first principlescovering areas like nondeterminism, evaluation approaches, and iteration cyclesthat can guide your work regardless of which models or frameworks you choose. Which multiagent frameworks?

Testing 174

Testing Data-driven Software Measurement 174

Analytics Vidhya

NOVEMBER 26, 2021

We will train various classification models and compare the performance metrics to extract useful insights. In this post, we will discuss the sentiment analysis problem. We have taken the Twitter US airline sentiment dataset for this empirical study. Twitter US Airline Sentiment dataset: […].

Machine Learning 343

Machine Learning Metrics Data Science Publishing 343

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.

Metrics 317

Metrics Modeling Analytics Deep Learning 317

Analytics Vidhya

JULY 25, 2024

Introduction Imagine that you are about to produce a Python package that has the potential to completely transform the way developers and data analysts assess their models. The trip begins with a straightforward concept: a flexible RAG evaluation tool that can manage a variety of metrics and edge circumstances.

Metrics 301

Metrics Modeling Management Analytics 301

datapine

NOVEMBER 15, 2022

1) What Are Product Metrics? 2) Types Of Product Metrics. 3) Product Metrics Examples You Can Use. 4) Product Metrics Framework. The right product performance metrics will give you invaluable insights into its health, strength and weaknesses, potential issues or bottlenecks, and let you improve it greatly.

Metrics 141

Metrics KPI Measurement Dashboards 141

CIO Business Intelligence

DECEMBER 10, 2024

Specify metrics that align with key business objectives Every department has operating metrics that are key to increasing revenue, improving customer satisfaction, and delivering other strategic objectives. Below are five examples of where to start. Gen AI holds the potential to facilitate that.

Sales 143

Sales Metrics Data-driven Measurement 143

O'Reilly on Data

JUNE 9, 2020

The biggest problem facing machine learning today isn’t the need for better algorithms; it isn’t the need for more computing power to train models; it isn’t even the need for more skilled practitioners. The development process doesn’t end with a model. It ends with that model that’s deployed.

Machine Learning 348

Machine Learning Metrics Modeling IT 348

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 363

Management Machine Learning Experimentation Metrics 363

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

Management 314

Management Machine Learning Metrics Modeling 314