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

The Data Visualisation Catalogue

DECEMBER 13, 2021

A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention. User Modeling and User-Adapted Interaction , 16(1), 1–30. Journal of Experimental Psychology: Applied, 4 (2), 119–138. Research Papers. Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways. Koh, E., & Franconeri, S.

Visualization 118

Visualization Experimentation Publishing Interactive 118

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Galbraith eventually opted to publish Cuckoo’s Calling through an acquaintance of sorts. but the publishing industry failed to see it. The AIgent was built with BERT, Google’s state-of-the-art language model.

Modeling 59

Modeling Metadata Publishing Sales 59

IBM Big Data Hub

FEBRUARY 1, 2024

Today, more than 130 central banks are actively exploring CBDCs and publishing periodic reports on the functional and non-functional requirements of CBDC platforms, including the evolving architectural considerations and the outcomes of their various CBDC experimentations.

Experimentation 101

Experimentation Modeling Finance Reporting 101

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 88

Cost-Benefit Visualization Modeling Machine Learning 88

DataRobot Blog

MARCH 16, 2023

We are offering customers rapid experimentation and value identification, with both code-first and no-code approaches. Erick Brethenoux, Anthony Mullen, Published 26 August 2022 The post A New Era of Value-Driven AI appeared first on DataRobot AI Platform.

Experimentation 52

Experimentation Data-driven Measurement Enterprise 52

Domino Data Lab

JANUARY 11, 2021

Arming data science teams with the access and capabilities needed to establish a two-way flow of information is one critical challenge many organizations face when it comes to unlocking value from their modeling efforts. Domino Data Lab and Snowflake: Better Together. Writing data from Domino into Snowflake.

Recreation/Entertainment 103

Recreation/Entertainment Data Science Data Warehouse Modeling 103

DataRobot

MARCH 10, 2021

Deploy the machine learning model into production. Recently published research papers show the danger of describing your AI systems as autonomous. The experimenters simulated experiences in online travel and online dating, varying the time people waited for a search result. Set the goal to be achieved or optimized.

Experimentation 145

Experimentation Machine Learning Key Performance Indicator Reporting 145

Smart Data Collective

FEBRUARY 4, 2020

Current R&D Models Provide Diminishing Returns. The greatest advantage of AI is that it can digest vast amounts of medical knowledge — from thousands of published reports and scientific papers, say — and devise novel predictions and formulations that would take human researchers years of inefficient experimentation to find.

Experimentation 100

Experimentation Testing Modeling Strategy 100

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

Data Science and Beyond

APRIL 5, 2021

Well, no one has compiled a meta-post of my public work from 2020 (that I know of), so it’s finally time to publish it myself. The world has adapted quickly, though it seems like Automattic’s globally-distributed model is still quite unusual. Remote work. Only time will tell. Sustainability. Technical work.

Experimentation 61

Experimentation Testing Publishing Measurement 61

CIO Business Intelligence

JUNE 28, 2023

Even among the companies permitting the tools, many are publishing stringent usage guidelines, and are proactively working with technology partners to accelerate access to enterprise-grade solutions with more robust security. Experimentation with a use case driven approach. Business fundamentals still apply.

Enterprise 52

Enterprise Experimentation Risk Data-driven 52

DataRobot Blog

AUGUST 10, 2022

Snowflake provides a state-of the-art data platform for collating and analysing workforce data, and with the combined addition of DataRobot Solution Accelerator models, trusts can have predictive models running with little experimentation — further accelerated by the wide range of supportive datasets available through the Snowflake Marketplace.

Forecasting 52

Forecasting Experimentation Data-driven Risk 52

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

Ontotext

NOVEMBER 2, 2023

However, it will take effort to formalize a shared semantic model that can be mapped to data assets, and turn unstructured data into a format that can be mined for insight. And while there is a great deal of experimentation underway, most organizations have only scratched the surface in a use-case-by-use-case fashion.

IT 69

IT Cost-Benefit Data-driven Technology 69

Insight

MARCH 27, 2019

The main benefits and features of DeepAugment are: Reduces the error rate of CNN models (showed 60% decrease in error for CIFAR10 on WRN-28–10) Saves time by automating the process 50 times faster than Google’s previous solution– AutoAugment The finished package is on PyPI. To learn more about how I built this, read on!

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

Sisense

MARCH 26, 2021

Newly published research shows that companies aren’t getting the most out of their analytics. Companies surveyed by Harvard Business Review Analytic Services (HBR) report that two of the most important strategic benefits of using data analytics are (1) identifying new revenue and business models and (2) becoming more innovative.

Analytics 98

Analytics Data-driven Dashboards Experimentation 98

Data Science 101

OCTOBER 15, 2023

Those vendors are safe due to established revenue elsewhere, but they are under pressure to publish, and you can set your watch to press releases (and generally well meaning if muddled media interpretation) at various cycles relating to public company reporting. This problem can be witnessed in the business strategies of QC companies.

Risk 107

Risk Marketing Consulting Technology 107

Ontotext

NOVEMBER 6, 2019

GraphDB Workbench is the interface for Ontotext’s semantic graph database, which provides the core infrastructure including modelling agility, data integration, relationship exploration and cross-enterprise semantic data publishing and consumption. The Plugins. The RDFRank plugin is one of the most popular plugins for GraphDB.

Experimentation 45

Experimentation Deep Learning Software Modeling 45

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. The DataOps way of publishing new products or updates is called continuous delivery, and it relies heavily on automated processes to do all the grunt work. Let’s take a look.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

The Unofficial Google Data Science Blog

JULY 22, 2020

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. Companies like Google [2], Amazon [3], and Microsoft [4] have all published scholarly articles on this topic.

Experimentation 95

Experimentation Statistics Testing Strategy 95

DataRobot Blog

MARCH 16, 2023

release, we’ve made it easy for you to rapidly prepare data, engineer new features and subsequently automate model deployment and monitoring into your Snowflake data landscape, all with limited data movement. We’ve tightened the loop between ML data prep , experimentation and testing all the way through to putting models into production.

Modeling 52

Modeling Experimentation Machine Learning Data-driven 52

Cloudera

APRIL 13, 2021

Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. We recently published a Cloudera Special Edition of Production Machine Learning For Dummies eBook. Don’t worry about building an ML model that’s flawless from the start.

Machine Learning 97

Machine Learning Enterprise Experimentation Modeling 97

AWS Big Data

OCTOBER 4, 2023

An Amazon DataZone domain contains an associated business data catalog for search and discovery, a set of metadata definitions to decorate the data assets that are used for discovery purposes, and data projects with integrated analytics and ML tools for users and groups to consume and publish data assets.

Metadata 96

Metadata Data Lake Publishing Data Governance 96

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).

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. In other words, your talk didn’t quite stand out enough to put onstage, but you still get “publish or perish” credits for presenting.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

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. And if the information you want doesn’t exist, the model will still attempt to create something plausible. These kinds of ensemble approaches are likely to become more common.

Modeling 111

Modeling Testing Cost-Benefit Experimentation 111

CIO Business Intelligence

SEPTEMBER 19, 2023

Columbia University professor David Rogers, author of Digital Transformation Playbook and The Digital Transformation Roadmap , published in September, says it doesn’t have to be that way. They should rather manage through experimentation. CIOs can help their enterprises in this area. Talent is not only about hiring but retaining also.

Digital Transformation 130

Digital Transformation Uncertainty Testing Enterprise 130

Ontotext

DECEMBER 30, 2022

9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. We have delivered technology and solutions to global leaders across several sectors: publishing (FT, Elsevier), financial services (S&P), pharma (AstraZeneca), government (UK Parliament) and others.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

Ontotext

DECEMBER 30, 2022

9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. We have delivered technology and solutions to global leaders across several sectors: publishing (FT, Elsevier), financial services (S&P), pharma (AstraZeneca), government (UK Parliament) and others.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

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 133

IT Risk Management Interactive Modeling 133

Insight

FEBRUARY 20, 2020

Since we are not using any features, there is no model to learn and thus no variance. Armed with enough training data and a good model, we expect that for two new inputs h and N our model is able to predict M better than chance. We don’t even need a machine learning model to predict the outcome. confidence. confidence.

Machine Learning 71

Machine Learning Experimentation Modeling Measurement 71

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

DataRobot Blog

FEBRUARY 15, 2023

In addition, it’s essential that models comply with regulations and treat customers fairly, making it more important than ever to monitor models in production. We also look at how collaboration is built into the core of the DataRobot AI platform so that your entire team can collaborate from business use case to model deployment.

Modeling 52

Modeling Machine Learning Cost-Benefit Metrics 52

Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. I was very comfortable with that definition, having spent my PhD years on several predictive modelling tasks, and having worked as a software engineer prior to that.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

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

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. My student and I recently published a research paper on this topic, which we summarized in our Radar article Teaching Programming in the Age of ChatGPT. Setting the Stage: Who Am I and What Am I Trying to Build?

Consulting 246

Consulting Interactive Software IT 246