The Unofficial Google Data Science Blog

JANUARY 16, 2018

We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. This simulation is based on the actual user network of GCP.

Testing 58

Testing Experimentation Measurement Modeling 58

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 Modeling Data-driven 135

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

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

AWS Big Data

AUGUST 21, 2023

For the demo, we’re using the Amazon Titan foundation model hosted on Amazon Bedrock for embeddings, with no fine tuning. It similarly codes the query as a vector and then uses a distance metric to find nearby vectors in the multi-dimensional space. With OpenSearch’s Search Comparison Tool , you can compare the different approaches.

Data Processing 92

Data Processing Experimentation Visualization Metrics 92

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

DataKitchen

OCTOBER 5, 2021

DataOps enables: Rapid experimentation and innovation for the fastest delivery of new insights to customers. The data scientist’s products are models and segmentations. Instead of focusing on a narrowly defined task with minimal testing and feedback, DataOps focuses on adding value. Create tests. What is DataOps.

Testing 157

Testing Dashboards Measurement Experimentation 157

CIO Business Intelligence

NOVEMBER 13, 2023

Transformational leaders must ensure their organizations have the expertise to integrate new technologies effectively and the follow-through to test and troubleshoot thoroughly before going live. This involves setting up metrics and KPIs and regularly reviewing them to identify areas for improvement.

Digital Transformation 143

Digital Transformation Cost-Benefit Experimentation Measurement 143

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

DataRobot

JUNE 10, 2021

Traditionally, experimentation and observation was the only way to understand the physical-chemical properties of the molecule. DataRobot’s AutoML uses different feature engineering techniques and a variety of machine learning algorithms to identify the best model for multilabel classification. With DataRobot this is a one-click step.

Metrics 52

Metrics Machine Learning Visualization Experimentation 52

AWS Big Data

JANUARY 9, 2024

Traditional lexical search, based on term frequency models like BM25, is widely used and effective for many search applications. Semantic search In semantic search, the search engine uses an ML model to encode text or other media (such as images and videos) from the source documents as a dense vector in a high-dimensional vector space.

Cost-Benefit 91

Cost-Benefit Visualization Modeling Machine Learning 91

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

Domino Data Lab

AUGUST 6, 2021

When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Develop: includes accessing and preparing data and algorithms, researching and development of models and experimentation. 4 Steps in the DSLC. Assess the Project.

Data Science 75

Data Science Testing Modeling Publishing 75

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 105

Advertising Recreation/Entertainment Experimentation Enterprise 105

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

AWS Big Data

MAY 2, 2023

If your updates to a dataset triggers multiple subsequent DAGs, then you can use the Airflow metric max_active_tasks_per_dag to control the parallelism of the consumer DAG and reduce the chance of overloading the system. Test the feature To test this feature, run the producer DAG. Removal of experimental Smart Sensors.

Testing 100

Testing Experimentation Management Metadata 100

Domino Data Lab

DECEMBER 4, 2020

Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. 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]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. SPC is the continuous testing of the results of automated manufacturing processes. SPC tests can do the same thing for the data flowing through your pipelines.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

Occam's Razor

MARCH 23, 2021

This is very hard to do, we now have a proven seven-step experimentation process, with one of the coolest algorithms to pick matched-markets (normally the kiss of death of any large-scale geo experiment). The first component is a gloriously scaled global creative pre-testing program. Matched market tests. The slow music.

Analytics 131

Analytics Scorecard Strategy Testing 131

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

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. Five Reasons And Awesome Testing Ideas.

KPI 124

KPI Metrics Measurement ROI 124

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

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 111

Cost-Benefit Metrics Insurance Measurement 111

CIO Business Intelligence

JUNE 27, 2022

Tyson: You’re coming up to two years since you added Sentry’s ability to monitor for performance which has some pretty fine-grained metrics in terms of identifying where bottlenecks are located in code. We rely heavily on automated testing. Tyson: That belief in your vision when it’s tested—that is tough! I thought, really?!

Software 115

Software Experimentation Testing Marketing 115

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. The No Test Gaps Principle Under the No Test Gaps Principle, it is unacceptable that LLMs are not tested holistically with a reproducible test suite before deployment.

Modeling 95

Modeling Testing Experimentation Risk Management 95

O'Reilly on Data

MAY 14, 2020

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

Management 363

Management Experimentation B2B Machine Learning 363

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.”. Debugging AI Products.

Management 246

Management Metrics Machine Learning Modeling 246

DataKitchen

APRIL 13, 2021

Testing and Data Observability. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Testing and Data Observability.

Testing 307

Testing Machine Learning Consulting Data Quality 307

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 154

Optimization Reporting Experimentation Metrics 154

Occam's Razor

DECEMBER 9, 2009

The four pronged real world tested probing and loaded with politics framework to find a home for Web Analytics: 1. Which physical organizational model will work best for you? My organization redesign plans have recommended either one of the three models. If you are executing on a centralized model be aware of the pros and cons.

Analytics 95

Analytics Data-driven Marketing Sales 95

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

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

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 141

Metrics Reporting Analytics Marketing 141

CIO Business Intelligence

MARCH 5, 2024

Many other platforms, such as Coveo’s Relative Generative Answering , Quickbase AI , and LaunchDarkly’s Product Experimentation , have embedded virtual assistant capabilities but don’t brand them copilots. Today, top AI-assistant capabilities delivering results include generating code, test cases, and documentation.

Cost-Benefit 124

Cost-Benefit Advertising Experimentation Software 124

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

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. A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Occam's Razor

JULY 18, 2011

The only requirement is that your mental model (and indeed, company culture) should be solidly rooted in permission marketing. You just have to have the right mental model (see Seth Godin above) and you have to… wait for it… wait for it… measure everything you do! Just to ensure you are executing against your right mental model.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137