Corinium

JUNE 6, 2019

Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. It is also important to have a strong test and learn culture to encourage rapid experimentation. What is the most common mistake people make around data?

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

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. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 89

Cost-Benefit Modeling Manufacturing Interactive 89

DataRobot

SEPTEMBER 22, 2021

You have to pick the right model to meet expected performance goals. Train it, test it, tune it. You have to know how to take your model into production, manage it over time, enable the business to consume predictions from it, and use it to make the right decisions. You have to make your AI explainable and trustworthy.

Experimentation 85

Experimentation Testing Modeling Machine Learning 85

The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

CIO Business Intelligence

JUNE 13, 2024

Proof that even the most rigid of organizations are willing to explore generative AI arrived this week when the US Department of the Air Force (DAF) launched an experimental initiative aimed at Guardians, Airmen, civilian employees, and contractors. It is not training the model, nor are responses refined based on any user inputs.

Testing 85

Testing Experimentation Data Processing Modeling 85

CIO Business Intelligence

MAY 16, 2023

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. AI Opportunities Generative AI is the basis for sophisticated AI models such as ChatGPT and Dall-E. Conversations suggest that AI is already transforming most major industries.

Advertising 86

Advertising Recreation/Entertainment Experimentation Enterprise 86

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

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. This integration is key in assuring that models evolve with the data – to avoid, for example, model drift. A/B testing). model packaging, deployment and serving.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

CIO Business Intelligence

OCTOBER 18, 2023

Of roughly 2,500 CIOs surveyed recently by Gartner, 9% say they have already deployed gen AI applications, and a staggering 55% say they will deploy large language models (LLMs) in production by the end of 2025. Snap, LexisNexis, and Lonely Planet are also developing and training LLM models, each leveraging their own data stored on AWS. “We

Risk 134

Risk Manufacturing Enterprise Technology 134

CIO Business Intelligence

JULY 31, 2023

As vendors add generative AI to their enterprise software offerings, and as employees test out the tech, CIOs must advise their colleagues on the pros and cons of gen AI’s use as well as the potential consequences of banning or limiting it. How has, say, ChatGPT hit your business model?” How is your business impacted by generative AI?

Strategy 95

Strategy Risk Enterprise Risk Management 95

DataRobot

APRIL 19, 2021

The model was worthless. It was embarrassing, but thank goodness my model was built just for fun, not for a mission-critical business application that could result in millions of dollars in losses or for a healthcare application that could affect life-or-death decisions. After the initial shock and disappointment, I reviewed my work.

Machine Learning 52

Machine Learning Visualization Data-driven Data Science 52

Occam's Razor

MARCH 23, 2021

The other dimension to consider is most Analtyics teams kick into gear after the campaign is concluded, after the customer interaction has taken place in the call center, and after the funds budgeted have already been spent. The first component is a gloriously scaled global creative pre-testing program. Matched market tests.

Analytics 131

Analytics Scorecard Strategy Testing 131

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. 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. Testing out a new feature.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. Capabilities Beyond Classic Jupyter for End-to-end Experimentation. Auto-scale compute.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

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

AUGUST 5, 2019

As more companies become model-driven , data scientists are uniquely positioned to drive innovation and to help their companies remain economically strong. the model context ) when building and using models enable data scientists to directly impact business problems. data munging, building models, etc.).

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

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

CIO Business Intelligence

JUNE 27, 2022

David Cramer: I love the open source community so I would build a lot of things in open source to interact with my peers. We rely heavily on automated testing. A lot of the current approaches feel very experimental and are tough to see as maintainable, so there’s certainly still room for growth here. How did that happen?

Software 98

Software Experimentation Testing Marketing 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, maybe they are open to some testing and exploration, but when it’s prime time, larger organizations won’t run the risk of putting something untested. Especially now. Maybe later.

Technology 59

Technology Risk Digital Transformation Measurement 59

CIO Business Intelligence

MAY 1, 2024

Gen AI takes us from single-use models of machine learning (ML) to AI tools that promise to be a platform with uses in many areas, but you still need to validate they’re appropriate for the problems you want solved, and that your users know how to use gen AI effectively. Pilots can offer value beyond just experimentation, of course.

Cost-Benefit 139

Cost-Benefit Metrics Insurance Measurement 139

bridgei2i

APRIL 28, 2020

And so that process with curation or identifying which data potentially is a leading indicator and then test those leading indicators. Are they going to look at, you know, maybe new business models using data? Now, the other thing that we find a number of companies are doing is looking for new revenue models. So, let me explain.

Enterprise 103

Enterprise Data Analytics Analytics Forecasting 103

CIO Business Intelligence

MAY 9, 2024

And because generative AI (genAI) is interactive and dialogue-based, it can help you get into a state of flow. Experimentation drives momentum: How do we maximize the value of a given technology? Via experimentation. AI changes the game. If the C-suite’s role is to lead by influence, the SWAT team’s role is to lead by execution.

Enterprise 99

Enterprise Experimentation Sales Optimization 99

Occam's Razor

AUGUST 12, 2013

than multi-channel attribution modeling. By the time you are done with this post you'll have complete knowledge of what's ugly and bad when it comes to attribution modeling. You'll know how to use the good model, even if it is far from perfect. Multi-Channel Attribution Models. Linear Attribution Model.

Modeling 161

Modeling Optimization Marketing Interactive 161

CIO Business Intelligence

APRIL 5, 2023

Midjourney, ChatGPT, Bing AI Chat, and other AI tools that make generative AI accessible have unleashed a flood of ideas, experimentation and creativity. Testing is another area that tends to get neglected, so automated unit test generation will help you get much broader test coverage.

Modeling 96

Modeling Testing Cost-Benefit Experimentation 96

O'Reilly on Data

SEPTEMBER 15, 2020

This in turn would increase the platform’s value for users and thus increase engagement, which would result in more eyes to see and interact with ads, which would mean better ROI on ad spend for customers, which would then achieve the goal of increased revenue and customer retention (for business stakeholders).

Cost-Benefit 300

Cost-Benefit Advertising ROI Machine Learning 300

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. But these Guardian polls appear to have been published on Microsoft properties with millions of visitors by automated systems with no human approval required.

IT 130

IT Risk Management Interactive Modeling 130

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

DataRobot

SEPTEMBER 14, 2021

And for those that do make it past the experimental stage, it typically takes over 18 months for the value to be realized. Accurate models today become inaccurate tomorrow. Models often degrade fast in production as they encounter unexpected conditions in the real world. Challenge to keep production models fair and ethical.

Machine Learning 94

Machine Learning Modeling Experimentation Visualization 94

O'Reilly on Data

JULY 25, 2023

I’m a professor who is interested in how we can use LLMs (Large Language Models) to teach programming. Swift Papers felt like a well-scoped project to test how well AI handles a realistic yet manageable real-world programming task. Setting the Stage: Who Am I and What Am I Trying to Build?

Consulting 246

Consulting Interactive Software IT 246

Occam's Razor

SEPTEMBER 13, 2016

Bonus #2: The Askers-Pukers Business Model. Hypothesis development and design of experimentation. Ok, maybe statistical modeling smells like an analytical skill. If these 50 pass the sniff test, send the survey. Bonus #2: The Askers-Pukers Business Model. Three thoughts that explain the Econsultancy/Lynchpin graph.

Modeling 127

Modeling Reporting Strategy Consulting 127

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

datapine

FEBRUARY 4, 2020

While your keyboard is burning and your fingers try to keep up with your brain and comprehend all the data you’re writing about, using an interactive online data visualization tool to set specific time parameters or goals you’ve been tracking can bring a lot of saved time and, consequently, a lot of saved money. 1) Marketing CMO report.

Reporting 280

Reporting Marketing Advertising Metrics 280

Occam's Razor

AUGUST 13, 2012

Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. I explain three different models (Online to Store, Across Multiple Devices, Across Digital Channels) and for each I've highlighted: 1. What's possible to measure.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

The Unofficial Google Data Science Blog

JULY 31, 2016

At the end of the semester, you administer a proficiency test to the students and compare test performance across the groups. For example, you might compare the average test score among students exposed to the new curriculum to the average score among the controls. The second source of dependence comes from student interactions.

Experimentation 42

Experimentation Testing Measurement Metrics 42

AWS Big Data

JULY 26, 2023

Using augmented ML search and generative AI with vector embeddings Organizations across all verticals are rapidly adopting generative AI for its ability to handle vast datasets, generate automated content, and provide interactive, human-like responses.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JULY 28, 2021

For companies creating models to scale, an enterprise Machine Learning Operation (MLOps) platform not only needs to support enterprise-grade development and production, it needs to follow the same standard process that data scientists use. Roles are assigned and work priorities are established before any research and development can begin.

Data Science 59

Data Science Enterprise Modeling Experimentation 59