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 162

Modeling Optimization Marketing Interactive 162

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). Media-Mix Modeling/Experimentation. Mobile content consumption, behavior along key metrics (time, bounces etc.) First, a quick techie lesson.

Metrics 143

Metrics Reporting Analytics Marketing 143

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. A complete DataOps program will have a unified, system-wide view of process metrics using a common data store. Datatron — Automates deployment and monitoring of AI models.

Testing 312

Testing Machine Learning Consulting Data Quality 312

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 250

Management Metrics Machine Learning Modeling 250

Rocket-Powered Data Science

JULY 6, 2021

MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. the monitoring of very important operational ML characteristics: data drift, concept drift, and model security).

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

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. That means: All of these metrics are off.

Modeling 125

Modeling Metrics Data Quality Data-driven 125

DataRobot Blog

DECEMBER 6, 2022

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Taking a Multi-Tiered Approach to Model Risk Management. Learn how to leverage Google BigQuery large datasets for large scale Time Series forecasting models in the DataRobot AI platform.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 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. The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%).

Enterprise 358

Enterprise Risk Modeling Manufacturing 358

datapine

FEBRUARY 4, 2020

Structure your metrics. As with any report you might need to create, structuring and implementing metrics that will tell an interesting and educational data-story is crucial in our digital age. That way you can choose the best possible metrics for your case. Regularly monitor your data. 1) Marketing CMO report.

Reporting 280

Reporting Marketing Advertising Metrics 280

CIO Business Intelligence

OCTOBER 10, 2024

A medical, insurance, or financial large language model (LLM) AI, built from scratch, can cost up to $20 million. Still, a 30% failure rate represents a huge amount of time and money, given how widespread AI experimentation is today. If a project isn’t hitting the metrics, the teams can decide whether to dump it or give it more time.

ROI 135

ROI Experimentation Cost-Benefit Metrics 135

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

IBM Big Data Hub

MAY 17, 2024

Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan. Cloud maturity models (or CMMs) are frameworks for evaluating an organization’s cloud adoption readiness on both a macro and individual service level.

Modeling 96

Modeling Cost-Benefit Optimization Digital Transformation 96

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

CIO Business Intelligence

JULY 11, 2023

In recent years, we have witnessed a tidal wave of progress and excitement around large language models (LLMs) such as ChatGPT and GPT-4. In short, providers must demonstrate that their models are safe and effective.

Modeling 95

Modeling Testing Experimentation Risk Management 95

Domino Data Lab

SEPTEMBER 10, 2020

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant. This means many projects get stuck in endless research and experimentation.

Modeling 101

Modeling Data Science IT Metrics 101

CIO Business Intelligence

APRIL 25, 2024

During the summer of 2023, at the height of the first wave of interest in generative AI, LinkedIn began to wonder whether matching candidates with employers and making feeds more useful would be better served with the help of large language models (LLMs).

IT 139

IT Experimentation Cost-Benefit Metrics 139

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

AWS Big Data

SEPTEMBER 5, 2024

In the context of Retrieval-Augmented Generation (RAG), knowledge retrieval plays a crucial role, because the effectiveness of retrieval directly impacts the maximum potential of large language model (LLM) generation. document-only) ~ 20%(bi-encoder) higher NDCG@10, comparable to the TAS-B dense vector model.

Metrics 111

Metrics Testing Experimentation Modeling 111

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. Key To Your Digital Success: Web Analytics Measurement Model. Web Data Quality: A 6 Step Process To Evolve Your Mental Model. "Engagement" Is Not A Metric, It's An Excuse. Experimentation and Testing: A Primer.

KPI 125

KPI Metrics Measurement ROI 125

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 143

Cost-Benefit Metrics Insurance Measurement 143

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 90

Machine Learning Modeling Metadata Recreation/Entertainment 90

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

CIO Business Intelligence

AUGUST 15, 2024

A virtual assistant may save employees time when searching for old documents or composing emails, but most organizations have no idea how much time those tasks have taken historically, having never tracked such metrics before, she says. However, foundational models will always have a place as the core backbone for the industry.”

ROI 143

ROI Cost-Benefit Experimentation Deep Learning 143

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. So as my tiny gift for you here are five experimentation and testing ideas for you. You can of course test different pretty images, why not try to reinvent your business model using testing? Rather than create prediction models (with faulty assumptions!)

Testing 113

Testing Experimentation Risk Optimization 113

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

CIO Business Intelligence

AUGUST 5, 2024

Healthcare Domain Expertise: It cannot be said enough that anyone developing AI-driven models for healthcare needs to understand the unique use cases and stringent data security and privacy requirements – and the detailed nuances of how this information will be used – in the specific healthcare setting where the technology will be deployed.

Experimentation 124

Experimentation Risk Metadata Data-driven 124

CIO Business Intelligence

OCTOBER 1, 2024

Organizations rolling out AI tools first need to set reasonable expectations and establish key metrics to measure the value of the deployment , he says. Sax has deployed AI on several internal projects, including its help desk functions, with the company training and customizing AI models itself. “My

ROI 131

ROI IT Measurement Experimentation 131

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. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

OCTOBER 21, 2013

I've gone through the five stages in the Kubler-Ross model. Bonus: For more on next steps and attribution modeling please see: Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models. ]. And of course our Acquisition, Behavior, Outcome metrics. Controlled experimentation. See Page Value there?

Optimization 156

Optimization Reporting Metrics Experimentation 156

CIO Business Intelligence

JULY 5, 2024

So, to maximize the ROI of gen AI efforts and investments, it’s important to move from ad-hoc experimentation to a more purposeful strategy and systematic approach to implementation. Define which strategic themes relate to your business model, processes, products, and services. This may impact some of your vendor selections as well.

ROI 124

ROI Experimentation Risk Strategy 124

CIO Business Intelligence

FEBRUARY 21, 2024

For example, top researchers at Florida State University are now developing innovative large language models (LLMs) to help advance research in areas like material science and healthcare — going beyond gen AI used by the general public. We developed a model to predict student outcomes based on metrics from historical evidence,” he says. “We

Recreation/Entertainment 135

Recreation/Entertainment Experimentation Data-driven Modeling 135

DataRobot Blog

MARCH 16, 2023

Why model-driven AI falls short of delivering value Teams that just focus model performance using model-centric and data-centric ML risk missing the big picture business context. We are also thrilled to share the innovations and capabilities that we have developed at DataRobot to meet and exceed those requirements.

Experimentation 98

Experimentation Data-driven Modeling Metadata 98

Occam's Razor

MARCH 23, 2010

Because every tool uses its own sweet metrics definitions, cookie rules, session start and end rules and so much more. If you don't kill 25% of your metrics each year, you are doing something wrong. Why do you think introducing a completely different set of numbers is going to make your life easier? Likely not. success measures.

Analytics 119

Analytics Consulting Metrics Experimentation 119

Occam's Razor

SEPTEMBER 30, 2013

Frameworks, because if I can teach someone a new mental model, a different way of thinking, they can be incredibly successful. the company are organized and incentivized (as in what metrics determine their bonus). Make sure your executive dashboards obsess about acquisition, behavior and outcome metrics. So fix that.

Marketing Analytics 156

Marketing Analytics Marketing Visualization Analytics 156

CIO Business Intelligence

JULY 8, 2022

After transforming their organization’s operating model, realigning teams to products rather than to projects , CIOs we consult arrive at an inevitable question: “What next?” Splitting these responsibilities without a clear vision and careful plan, however, can spell disaster, reversing the progress begotten by a new operating model.

Cost-Benefit 122

Cost-Benefit Measurement Modeling Metrics 122

DataRobot Blog

SEPTEMBER 30, 2022

How do you track the integrity of a machine learning model in production? Model Observability can help. By tracking service, drift, prediction data, training data, and custom metrics, you can keep your models and predictions relevant in a fast-changing world. Model Observability Features.

Slice and Dice 52

Slice and Dice Modeling Metrics Experimentation 52

DataRobot Blog

OCTOBER 18, 2022

Monitoring and Managing AI Projects with Model Observability. Model Observability – the ability to track key health and service metrics for models in production – remains a top priority for AI-enabled organizations. DataRobot Booth at Big Data & AI Toronto 2022.

Big Data 96

Big Data Forecasting Experimentation Metrics 96