Ontotext

FEBRUARY 14, 2024

Ivory tower modeling We’ve seen too many models developed by isolated ontologists that don’t survive the first battle with the data. There’s a famous saying by a statistician, George Box, “All models are wrong, but some are useful.” ” So, how do you know whether your model is useful?

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

DataRobot Blog

JANUARY 17, 2023

Recently, a prospective customer asked me how I reconcile the fact that DataRobot has multiple very successful investment banks using DataRobot to enhance the P&L of their trading businesses with my comments that machine learning models aren’t always great at predicting financial asset prices. For price discovery (e.g.,

Machine Learning 87

Machine Learning Marketing Experimentation Modeling 87

Business Over Broadway

AUGUST 4, 2020

Experimentation and iteration to improve existing ML models (39%). These top work activities included influencing business decisions, building prototypes to expand machine learning to new areas and improving ML models. Build prototypes to explore applying machine learning to new areas (52%).

Machine Learning 145

Machine Learning Data Science Statistics Experimentation 145

O'Reilly on Data

APRIL 19, 2021

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. It’s possible that pandemic-induced boredom led more people to respond, but we doubt it. Executive Summary. Respondents.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. They cannot process language inputs generally. See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

CIO Business Intelligence

JUNE 28, 2023

Belcorp operates under a direct sales model in 14 countries. As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.

Digital Transformation 93

Digital Transformation Cost-Benefit Informatics Data mining 93

DataRobot Blog

JULY 28, 2022

Companies are emphasizing the accuracy of machine learning models while at the same time focusing on cost reduction, both of which are important. In addition to the accuracy of the models we built, we had to consider business metrics, cost, interpretability, and suitability for ongoing operations. Sensor Data Analysis Examples.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

CIO Business Intelligence

MARCH 21, 2022

The more high-quality data available to data scientists, the more parameters they can include in a given model, and the more data they will have on hand for training their models. It doesn’t conform to a data model but does have associated metadata that can be used to group it. Data scientist job description.

Unstructured Data 136

Unstructured Data Data Analytics Analytics Structured Data 136

Occam's Razor

MAY 22, 2006

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

Experimentation 103

Experimentation Testing Optimization Measurement 103

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Features: DistilBERT Text Embeddings Once I had a raw dataset, I could begin engineering features and building a natural language processing (NLP) model.

Modeling 59

Modeling Metadata Publishing Sales 59

FineReport

DECEMBER 11, 2019

– Head First Data Analysis: A learner’s guide to big numbers, statistics, and good decisions. The big news is that we no longer need to be proficient in math or statistics, or even rely on expensive modeling software to analyze customers. For a while, everyone seems to have begun to learn data analysis.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. Continuous pipeline monitoring with SPC (statistical process control). But data, at best, can only be a powerful vehicle, or a reliable GPS system. Well, that’s obvious, you say.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

DataRobot

JANUARY 13, 2022

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. It’s easy to deploy, monitor, and manage models in production and react to changing conditions. And any predictive model can become an AI app in minutes—no coding required.

ROI 52

ROI Machine Learning Experimentation Data Science 52

O'Reilly on Data

MARCH 23, 2023

It’s important to understand that ChatGPT is not actually a language model. It’s a convenient user interface built around one specific language model, GPT-3.5, is one of a class of language models that are sometimes called “large language models” (LLMs)—though that term isn’t very helpful. It has helped to write a book.

IT 262

IT Modeling Testing Risk 262

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Domino Data Lab

JANUARY 24, 2019

These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. These tools support a breadth of use cases including data science, data engineering, and model operations. Proprietary (often GUI-driven) data science platforms.

Data Science 65

Data Science Marketing Data-driven Statistics 65

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Among these, only statistical uncertainty has formal recognition.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

FineReport

DECEMBER 19, 2019

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. Its designer interface is similar to Excel.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Ontotext

APRIL 3, 2024

Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. So, let’s start with our first question. We all know that for any initiative to succeed, we need the attention and the blessing of company executives. What are your thoughts about it? A graph can do that.

Technology 59

Technology Risk Digital Transformation Measurement 59

Occam's Razor

MARCH 23, 2021

Data Warehousing. And, a thousand other silos (depending on your company size). One outcome of this reality is that while every team works hard to do their very best work, it is rare that they earn strategic influence from their work. That’s not really surprising, if your view of your scope is narrow… Your impact will be narrow as well.

Analytics 131

Analytics Scorecard Strategy Testing 131

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. However, the downside of using a larger unit of randomization is that we lose experimental power. This simulation is based on the actual user network of GCP.

Testing 58

Testing Experimentation Measurement Modeling 58

Domino Data Lab

DECEMBER 4, 2020

NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. PharmaceUtical Modeling And Simulation (or PUMAS) is a suite of tools to perform quantitative analytics for pharmaceutical drug development [2]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Data Science and Beyond

DECEMBER 23, 2018

The book focuses on randomised controlled trials and well-defined interventions as the basis of causal inference from both experimental and observational data. Unlike some other resources, Causal Inference doesn’t appear to be too dogmatic about the framework used for modelling causality.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Domino Data Lab

OCTOBER 2, 2019

As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences. You can use a regression model. A complementary Domino project is available. . Introduction. We’d like to make stronger statements. Bad weather can cause you to be late for work. Experiments.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. In this post we discuss the problem, a solution, and practical considerations.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Occam's Razor

AUGUST 22, 2011

Key To Your Digital Success: Web Analytics Measurement Model. Web Data Quality: A 6 Step Process To Evolve Your Mental Model. Build A Great Web Experimentation & Testing Program. Experimentation and Testing: A Primer. Mobile Marketing and Analytics: Click-to-Call Mobile Ad Campaigns. What's The Fix? Got Surveys?

KPI 124

KPI Metrics Measurement ROI 124

Occam's Razor

APRIL 8, 2013

Let's listen in as Alistair discusses the lean analytics model… The Lean Analytics Cycle is a simple, four-step process that shows you how to improve a part of your business. Another way to find the metric you want to change is to look at your business model. But it is not routine. So, how do we fix this problem?

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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. Maybe it is Search, Email and Facebook.

Measurement 119

Measurement Advertising Testing Marketing 119

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Remember that digital transformation is about transforming your business and operating models with technology.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

CIO Business Intelligence

DECEMBER 6, 2023

Some of the most vocal complaints about generative AI have come from authors and artists unhappy at having their work used to train large language models (LLMs) without permission. OpenAI’s recent announcement of custom ChatGPT versions make it easier for every organization to use generative AI in more ways, but sometimes it’s better not to.

IT 132

IT Risk Management Interactive Modeling 132

Occam's Razor

JULY 24, 2018

work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps , metrics ladders of awesomeness , and… so… much… more. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more. The Implications of Complexity.

Analytics 93

Analytics Metrics Strategy Measurement 93

Occam's Razor

JUNE 4, 2018

I mean developing and inserting a subtle collection of gentle nudges that can help increase the conversion rate by a statistically significant amount. By every indicator available, ecommerce is continuing to grow at an insane speed. Still, I’m heartbroken that some the simplest elements of ecommerce stink so much. youarekillingme.

Strategy 124

Strategy Cost-Benefit Testing Sales 124

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Occam's Razor

SEPTEMBER 13, 2016

Bonus #2: The Askers-Pukers Business Model. Hypothesis development and design of experimentation. Analysis Ninjas are valued less than I would prefer for them to be. The post is also sourced from a recent edition of my newsletter, The Marketing – Analytics Intersect. Three thoughts that explain the Econsultancy/Lynchpin graph.

Modeling 127

Modeling Reporting Strategy Consulting 127