Smart Data Collective

MARCH 27, 2020

Here is the type of data insurance companies use to measure a client’s potential risk and determine rates. Statistics show that married people have fewer car accidents than singletons. Insurance companies have access to crime statistics and can track the number of car theft and break-ins per neighborhood. Demographics.

Insurance 107

Insurance Measurement Risk Cost-Benefit 107

IBM Big Data Hub

APRIL 13, 2023

After developing a machine learning model, you need a place to run your model and serve predictions. If your company is in the early stage of its AI journey or has budget constraints, you may struggle to find a deployment system for your model. Also, a column in the dataset indicates if each flight had arrived on time or late.

Modeling 88

Modeling Statistics Machine Learning Testing 88

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

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 190

Machine Learning Modeling Testing Risk Management 190

Domino Data Lab

JULY 1, 2021

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111

Modeling Statistics Testing Metrics 111

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 127

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 127

Smarten

APRIL 25, 2024

Artificial Intelligence (AI) and Machine Learning (ML) elements support Citizen Data Scientists and help users prepare data, achieve automated data insights and create, share and use predictive models. Users can harness the power of statistics and machine learning to uncover hidden insights and improve the overall quality of your data.

Data Quality 62

Data Quality Analytics Machine Learning Visualization 62

IBM Big Data Hub

APRIL 9, 2024

Preparing and annotating data IBM watsonx.data helps organizations put their data to work, curating and preparing data for use in AI models and applications. “For the Masters we use 290 traditional AI models to project where golf balls will land,” says Baughman. ” Watsonx.ai ” Watsonx.ai

Management 84

Management IT Machine Learning Metrics 84

DataKitchen

MARCH 12, 2024

I used to be very, very careful when changing anything data-related, but I think you’re starting to see that it’s not that big a deal anymore because [if something goes wrong] we can now figure out what happened pretty quickly, and we can adjust it.“

Metrics 120

Metrics Software Cost-Benefit Testing 120

CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance.

Data Analytics 130

Data Analytics Diagnostic Analytics Management Analytics 130

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 86

Metrics Machine Learning Risk Statistics 86

Domino Data Lab

NOVEMBER 17, 2020

High throughput screening technologies have been developed to measure all the molecules of interest in a sample in a single experiment (e.g., Predictive models fit to noise approach 100% accuracy. Each of these behaviors wreak havoc on statistical analyses. Guest Post by Bill Shannon, Founder and Managing Partner of BioRankings.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

KDnuggets

JANUARY 10, 2020

Finding a deep learning model to perform well is an exciting feat. But, might there be other -- less complex -- models that perform just as well for your application?

Deep Learning 100

Deep Learning Statistics Measurement Modeling 100

datapine

JANUARY 24, 2021

Additionally, incorporating a decision support system software can save a lot of company’s time – combining information from raw data, documents, personal knowledge, and business models will provide a solid foundation for solving business problems. There are basically 4 types of scales: *Statistics Level Measurement Table*.

IT 317

IT Statistics KPI Data-driven 317

O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. And last is the probabilistic nature of statistics and machine learning (ML).

Risk 359

Risk Modeling Data Processing Software 359

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Domino Data Lab

JULY 17, 2021

The system here will identify, via some meaningful sense, which existing speakers’ model does the utterance match. If the unknown utterance is spoken by a speaker outside the list of existing speakers, the model will nonetheless map it to some speaker from that list. The existing applications of person authentication include :-.

Modeling 64

Modeling Testing Machine Learning Measurement 64

DataKitchen

NOVEMBER 18, 2022

Observability users are then able to see and measure the variance between expectations and reality during and after each run. Real-time details are not the only purpose for setting and measuring against expectations. the data, the models, the integrations, the reports, and the outputs that your customers see?are

Testing 130

Testing Statistics Measurement Metrics 130

Smarten

JUNE 25, 2018

This article provides a brief explanation of the definition and uses of the Descriptive Statistics algorithms. What is a Descriptive Statistics? Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data.

Statistics 40

Statistics Enterprise Measurement Business Intelligence 40

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Before I continue, it’s important to emphasize that machine learning is much more than building models. Model lifecycle management.

Machine Learning 220

Machine Learning Risk Management Statistics 220

Smarten

MARCH 19, 2024

Statistical and analytical experts will tell you that there are three primary factors that can help you decide on the metrics to use for your analysis: The type of data you want to analyze – Understanding the data type can help you decide whether you need to consider a binary approach or look at categories, etc.

Analytics 57

Analytics Statistics Visualization Metrics 57

CIO Business Intelligence

JUNE 14, 2023

Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers that you have the right skillset. If you’re looking to get an edge on a data analytics career, certification is a great option. The number of data analytics certs is expanding rapidly.

Big Data 122

Big Data Data Analytics Analytics Predictive Modeling 122

O'Reilly on Data

JULY 28, 2020

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). Any metric can and will be abused.

Marketing 362

Marketing Experimentation Metrics Testing 362

datapine

NOVEMBER 15, 2022

Product metrics are quantitative performance measurements used by businesses to gain insights into various areas like product development, pricing, quality, and more. They can evaluate architectural measures, quality measures, software complexity, or functional size. What Are Product Metrics?

Metrics 141

Metrics KPI Measurement Dashboards 141

Analytics Vidhya

JUNE 26, 2023

The surge of Big Data has ushered in a new era, where businesses grapple with massive amounts of data measured in petabytes […] The post What is the Difference Between Data Science and Machine Learning? They are utilized by various entities, ranging from novice computer science students to major organizations like Netflix and Amazon.

Machine Learning 243

Machine Learning Data Science Measurement Big Data 243

DataRobot Blog

SEPTEMBER 1, 2022

For example, a recent IDC study 1 shows that it takes about 290 days on average to deploy a model into production from start to finish. Once you move your model into production, you need to monitor and manage your models to ensure that you can trust predictions and turn them into the right business decisions.

Metrics 145

Metrics Statistics Modeling Data Science 145

Domino Data Lab

OCTOBER 7, 2020

The Curse of Dimensionality , or Large P, Small N, ((P >> N)) , problem applies to the latter case of lots of variables measured on a relatively few number of samples. Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Rocket-Powered Data Science

MARCH 19, 2021

Insights discovery from ubiquitous data collection (via the tens of billions of connected devices that will be measuring, monitoring, and tracking nearly everything internally in our business environment and contextually in the broader market and global community) is ultimately about value creation and business outcomes.

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Mathematics, statistics, and programming are pillars of data science. Statistics.

Data Science 104

Data Science Statistics Deep Learning Machine Learning 104

Smart Data Collective

SEPTEMBER 13, 2023

These screens can collect data at a monumental scale: tracking eye movements, calculating linger time, measuring crowd density, and even observing emotional reactions. These can range from straightforward statistical models to advanced machine learning algorithms.

Big Data 112

Big Data Analytics Interactive Advertising 112

CIO Business Intelligence

JANUARY 17, 2024

To do this, Ipsos uses large language models to generate cutting-edge insights built using AI capabilities, complemented by its data science models and tailored to the services it offers,” says Mohammed. According to Mohammed, the results of this digital transformation journey are measurable and impressive. js and React.js.

Digital Transformation 111

Digital Transformation Dashboards Reporting Data Science 111

Occam's Razor

SEPTEMBER 19, 2011

How do you get over the frustration of having done attribution modeling and realizing that it is not even remotely the solution to your challenge of using multiple media channels? You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. ask for a raise. It is that simple.

Measurement 119

Measurement Advertising Testing Marketing 119

CIO Business Intelligence

JUNE 16, 2022

A major reason for this overwhelming lack of success is the failure to establish an operational model aligned with a world in which strategic pivots need to happen quickly. Value Stream Management (VSM) has become increasingly prevalent as the best operational model for those few organizations that have successfully transformed.

Digital Transformation 128

Digital Transformation Statistics Sales Measurement 128

Jen Stirrup

NOVEMBER 15, 2022

The foundation should be well structured and have essential data quality measures, monitoring and good data engineering practices. Of course, the findings need to add value, but how do we measure this success? Measures can be financial, tying in with the business strategy. After all, it can sound a bit woolly!

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

Occam's Razor

SEPTEMBER 29, 2009

One of the ultimate excuses for not measuring impact of Marketing campaigns is: "Oh, that's just a branding campaign." It is criminal not to measure your direct response campaigns online. I also believe that a massively under appreciated opportunity exists to truly measure impact of branding campaigns online.

Measurement 128

Measurement Metrics Analytics Marketing 128

DataKitchen

MAY 10, 2021

Since 2008, teams working for our founding team and our customers have delivered 100s of millions of data sets, dashboards, and models with almost no errors. Best practices include continuous monitoring of machine learning models for degradations in accuracy. . Week after week, it is measured with a million rows.

Testing 214

Testing Manufacturing Data Quality Statistics 214