Analytics Vidhya

AUGUST 28, 2021

The post Create a Python App to Measure Customer Lifetime Value (CLV) appeared first on Analytics Vidhya. Every industry has […].

Measurement 364

Measurement Data Science Publishing Analytics 364

Analytics Vidhya

DECEMBER 16, 2020

Introduction There are so many performance evaluation measures when it comes to. 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.

Measurement 306

Measurement Modeling Data Science Publishing 306

Analytics Vidhya

DECEMBER 2, 2024

As indicated in machine learning and statistical modeling, the assessment of models impacts results significantly. Meet the F-Beta Score, a more unrestrictive measure that let the user weights precision over recall or […] The post What is F-Beta Score?

Machine Learning 208

Machine Learning Statistics Measurement Modeling 208

Analytics Vidhya

MARCH 30, 2021

The post Interpretation of Performance Measures to Evaluate Models appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction In the last year of my bachelor’s degree, I.

Measurement 306

Measurement Modeling Data Science Publishing 306

O'Reilly on Data

DECEMBER 12, 2019

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Residuals are a numeric measurement of model errors, essentially the difference between the model’s prediction and the known true outcome. 2] The Security of Machine Learning. [3] Residual analysis.

Machine Learning 273

Machine Learning Modeling Testing Risk Management 273

Analytics Vidhya

DECEMBER 2, 2020

Introduction One of the most used matrices for measuring model performance is. The post A Measure of Bias and Variance – An Experiment appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon.

Measurement 293

Measurement Data Science Publishing Modeling 293

O'Reilly on Data

JULY 15, 2019

As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. Machine learning developers are beginning to look at an even broader set of risk factors. Sources of model risk.

Machine Learning 49

Machine Learning Management Enterprise Risk Management 49

Analytics Vidhya

SEPTEMBER 29, 2022

Introduction Various contemporary computer science and machine learning applications use multidimensional datasets encompassing a single expansive coordinate system. This article was published as a part of the Data Science Blogathon.

Machine Learning 352

Machine Learning Measurement Data Science Publishing 352

Analytics Vidhya

MARCH 18, 2023

Introduction The advent of the internet and the potential for mass quantitative and qualitative data collection altered the desire for and potential for measuring processes other than those in human resources.

Management 319

Management Machine Learning Data Collection Measurement 319

Smart Data Collective

FEBRUARY 16, 2023

We have talked extensively about some of the changes machine learning has introduced to the marketing profession. According to one analysis, companies that used machine learning in their marketing strategies boosted sales by up to 50%. How Can Machine Learning Boost Your Social Media Marketing ROI?

Machine Learning 140

Machine Learning ROI Marketing Advertising 140

Smart Data Collective

MARCH 22, 2022

Smart homes are getting smarter with machine learning. Some smart devices can ‘learn’ your preferences and run on autopilot by predicting your routines. This is made possible by machine learning. What is machine learning? The more a program learns, the more accurate the results.

Machine Learning 139

Machine Learning Data-driven Software Measurement 139

O'Reilly on Data

MARCH 31, 2020

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.

Management 363

Management Machine Learning Experimentation Metrics 363

Smart Data Collective

JANUARY 16, 2023

Companies around the world are projected to spend over $300 billion on machine learning technology by 2030. There are a growing number of reasons that companies are investing in machine learning, but digital marketing is at the top of the list. SEO, in particular, relies more heavily on machine learning these days.

Machine Learning 134

Machine Learning Marketing Cost-Benefit Optimization 134

Smart Data Collective

NOVEMBER 7, 2023

If you’re eager to monetize the web hosting services you offer to third party site owners, or you have a selection of self-hosted sites which you are eager to wring more cash out of, then machine learning could be the answer. This is where machine learning from top developers comes into play.

Data Processing 124

Data Processing Machine Learning Measurement Optimization 124

CIO Business Intelligence

JANUARY 23, 2024

Regardless of where organizations are in their digital transformation, CIOs must provide their board of directors, executive committees, and employees definitions of successful outcomes and measurable key performance indicators (KPIs). He suggests, “Choose what you measure carefully to achieve the desired results.

Measurement 141

Measurement Digital Transformation KPI Metrics 141

CIO Business Intelligence

OCTOBER 22, 2024

Singapore has rolled out new cybersecurity measures to safeguard AI systems against traditional threats like supply chain attacks and emerging risks such as adversarial machine learning, including data poisoning and evasion attacks.

Machine Learning 119

Machine Learning Measurement Risk IT 119

O'Reilly on Data

JANUARY 29, 2019

A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts. We found companies were planning to use deep learning over the next 12-18 months. On the other hand, we wanted to measure the sophistication of their use of these components.

Deep Learning 258

Deep Learning Machine Learning Data Science Metadata 258

CIO Business Intelligence

JANUARY 6, 2025

Wetmur says Morgan Stanley has been using modern data science, AI, and machine learning for years to analyze data and activity, pinpoint risks, and initiate mitigation, noting that teams at the firm have earned patents in this space. I am excited about the potential of generative AI, particularly in the security space, she says.

IT 140

IT Cost-Benefit Measurement Experimentation 140

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

Additionally, while the tools available at the time enabled data teams to respond to quality issues, they did not provide a way to identify quality thresholds or measure improvement, making it difficult to demonstrate to the business the value of time spent remedying data-quality problems. With

Data Quality 130

Data Quality Dashboards Data-driven Software 130

CIO Business Intelligence

OCTOBER 24, 2024

Then there’s reinforcement learning, a type of machine learning model that trains algorithms to make effective cybersecurity decisions. Organizations can even take pre-emptive steps to stop future attacks before they happen with AI’s predictive capabilities.

Risk 128

Risk Measurement Optimization Machine Learning 128

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. In addition, the Research PM defines and measures the lifecycle of each research product that they support.

Management 364

Management Experimentation B2B Machine Learning 364

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Why aren’t traditional software tools sufficient?

Modeling 235

Modeling Machine Learning Testing Metrics 235

O'Reilly on Data

FEBRUARY 11, 2019

Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning. In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Deep Learning.

Big Data 260

Big Data Technology Machine Learning Deep Learning 260

Analytics Vidhya

SEPTEMBER 30, 2022

Introduction Evaluation metrics are used to measure the quality of the model. This article was published as a part of the Data Science Blogathon. Selecting an appropriate evaluation metric is important because it can impact your selection of a model or decide whether to put your model into production.

Metrics 397

Metrics Measurement Data Science Publishing 397

David Menninger's Analyst Perspectives

JANUARY 15, 2025

So, it is essential to incorporate external data in forecasting, planning and budgeting, especially for predictive analytics and machine learning to support artificial intelligence. Enterprises do not operate in a vacuum, and things happening outside an organizations walls directly impact performance.

Predictive Modeling 130

Predictive Modeling Forecasting Predictive Analytics Statistics 130

O'Reilly on Data

MARCH 25, 2025

People have been building data products and machine learning products for the past couple of decades. How will you measure success? So now we have a user persona, several scenarios, and a way to measure success. Business value : Align outputs with business metrics and optimize workflows to achieve measurable ROI.

Testing 168

Testing Data-driven Software Measurement 168

Analytics Vidhya

AUGUST 5, 2022

Measuring the strength of that relationship […]. Introduction One of the most important applications of Statistics is looking into how two or more variables relate. Hypothesis testing is used to look if there is any significant relationship, and we report it using a p-value.

Statistics 375

Statistics Measurement Testing Data Science 375

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. . Collaboration and Sharing.

Testing 300

Testing Machine Learning Consulting Data Science 300

CIO Business Intelligence

SEPTEMBER 26, 2023

But because electricity consumption was easy to gauge, there was no urgency for measuring current and low voltage power flows. But the measuring solution was complex and required frequent manual adaptions as solar PV systems increased. Without real-time power measurements, estimated power values were being used.

Machine Learning 105

Machine Learning Forecasting Measurement Data Warehouse 105

CIO Business Intelligence

NOVEMBER 27, 2024

The analytics that drive AI and machine learning can quickly become compliance liabilities if security, governance, metadata management, and automation aren’t applied cohesively across every stage of the data lifecycle and across all environments.

Data Governance 132

Data Governance Risk Insurance Metadata 132

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks. General concerns.

Modeling 275

Modeling Machine Learning Predictive Modeling Consulting 275

O'Reilly on Data

SEPTEMBER 15, 2020

Technical sophistication: Sophistication measures a team’s ability to use advanced tools and techniques (e.g., PyTorch, TensorFlow, reinforcement learning, self-supervised learning). Technical competence: Competence measures a team’s ability to successfully deliver on initiatives and projects. Conclusion.

Advertising 359

Advertising Cost-Benefit ROI Machine Learning 359

Analytics Vidhya

JULY 15, 2024

Think of time complexity as the clock ticking away, measuring how long an algorithm takes to complete based on the size of its input. Introduction Have you ever wondered what makes some algorithms faster and more efficient than others? It all boils down to two crucial factors: time and space complexity. appeared first on Analytics Vidhya.

Measurement 319

Measurement Analytics IT Machine Learning 319

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI In this post, we shed some light on various efforts toward generating data for machine learning (ML) models. Machine learning applications rely on three main components: models, data, and compute.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

CIO Business Intelligence

MAY 23, 2024

There is measurable progress, however, as data from the company’s connected products are collected in its own platform, where customers have access to information via a portal. “In The company is also applying machine learning (ML) to gather information from various public sources that can be used internally for market and product analysis.

Digital Transformation 111

Digital Transformation Measurement Cost-Benefit Data Warehouse 111

Rocket-Powered Data Science

MARCH 19, 2021

One CIO said it this way , “If CIOs invested in machine learning three years ago, they would have wasted their money. The determination of winners and losers in the data analytics space is a much more dynamic proposition than it ever has been. But if they wait another three years, they will never catch up.”

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

AWS Big Data

JANUARY 15, 2025

Improve accuracy and resiliency of analytics and machine learning by fostering data standards and high-quality data products. In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications.

IoT 100

IoT Machine Learning Metadata Data-driven 100