CIO Business Intelligence

OCTOBER 11, 2022

Built on Amazon SageMaker , a service to build, train, and deploy ML models, AI Bench has accelerated the pace of innovation and reduced the barrier of entry for machine learning across AstraZeneca. . “We More quickly moving from ideas to insights has aided new drug development and the clinical trials used for testing new products.

Machine Learning 145

Machine Learning Data-driven Testing Data Science 145

CIO Business Intelligence

JUNE 7, 2022

To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more. Business analytics uses data analytics techniques, including data mining, statistical analysis, and predictive modeling, to drive better business decisions.

Data Analytics 132

Data Analytics Diagnostic Analytics Management Analytics 132

Smart Data Collective

JANUARY 27, 2021

Conversely, if predictive analytics models suggest that the value of a cryptocurrency price is likely to decrease, more investors are likely to sell off their cryptocurrency holdings. They tended to avoid using technical analysis models, because those models don’t tend to hold up well with traditional securities, such as stocks and bonds.

Machine Learning 139

Machine Learning Forecasting Predictive Analytics Marketing 139

CIO Business Intelligence

OCTOBER 18, 2023

Starting in 2015, the company began to digitalize all sales and after-sales processes, a purpose reinforced by a promotion of synergies between distribution channels that led Nationale-Nederlanden to become an omnichannel company, which made it easier for customers to choose where, how, and when to engage with it. “The

Digital Transformation 119

Digital Transformation Insurance Interactive Sales 119

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

Smart Data Collective

FEBRUARY 4, 2020

Current R&D Models Provide Diminishing Returns. Phase 0 is the first to involve human testing. Phase I involves dialing-in the proper dosage and further testing in a larger patient pool. In a report on the failure rates of drug discovery efforts between 2013 and 2015, Richard K. Some of the pieces are missing.

Experimentation 100

Experimentation Testing Modeling Strategy 100

Occam's Razor

JANUARY 9, 2012

Our mental model has not shifted enough to the existing reality. And yet it is the rare company that is able to get over its mental model from the real (old) world and try imaginative things in the digital world where the rules are different and stacked in your favor. The 2015 Digital Marketing Rule Book. Got your own?

Marketing 140

Marketing B2B Measurement Risk 140

IBM Big Data Hub

AUGUST 11, 2023

These insights can help drive decisions in business, and advance the design and testing of applications. Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights.

Data Science 55

Data Science Machine Learning Cost-Benefit Deep Learning 55

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

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

CIO Business Intelligence

SEPTEMBER 6, 2022

It launched its first online-only brand, Very, in 2009 and finally abandoned its printed catalogs to go all-in online in 2015. It was very fragmented, and I brought it together into a hub-and-spoke model.”. The new model enables Very to design once and deploy everywhere, while maintaining a product focus. Where do we store it?

IT 87

IT Forecasting Data Lake Enterprise 87

CIO Business Intelligence

JULY 6, 2023

Spreading the news Telecom provider AT&T began trialing RPA in 2015 to decrease the number of repetitive tasks, such as order entry, for its service delivery group. GPT-4 (Generative Pre-trained Transformer 4) is a multimodal large language model created by OpenAI, an AI research laboratory. Pilot to accelerate results.

Cost-Benefit 116

Cost-Benefit Technology Unstructured Data Testing 116

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

AWS Big Data

JULY 26, 2023

The vector engine supports the popular distance metrics such as Euclidean, cosine similarity, and dot product, and can accommodate 16,000 dimensions, making it well-suited to support a wide range of foundational and other AI/ML models. Carl has been with Amazon Elasticsearch Service since before it was launched in 2015.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

AWS Big Data

JUNE 6, 2023

In order to mature our data marts, it became clear that we needed to provide Analysts and other data consumers with all tracked digital analytics data in our DWH as they depend on it for analyses, reporting, campaign evaluation, product development and A/B testing. What we needed was a data pipeline from BigQuery to our Data Lake and the DWH.

Analytics 55

Analytics Data Lake Testing Optimization 55

IBM Big Data Hub

MARCH 29, 2024

GraphQL GraphQL is a query language and API runtime that Facebook developed internally in 2012 before it became open source in 2015. Tools like GraphiQL and GraphQL Playground provide powerful, in-browser, integrated development environments (IDEs) for exploring and testing GraphQL APIs.

Interactive 75

Interactive Digital Transformation Management Optimization 75

Smart Data Collective

MAY 19, 2021

Some of the most important benefits of big data include: Forecasting future demand with predictive analytics Using data analytics to better understand customer demographics Using deep learning technology to fine-tune business models to lower costs Use AI to conduct new permutations when testing new clean energy technology to bolster its efficiency.

Data Analytics 89

Data Analytics Analytics Big Data Predictive Analytics 89

CIO Business Intelligence

MAY 17, 2022

In 2015, Google’s Photos app labeled pictures of black people as “gorillas.” If a correlation uncovered by the algorithm is weak, doctors might be subjecting patients to unnecessary tests that would be a waste of time and money for the hospital. Was the problem in the model, or in its implementation?

Risk 144

Risk Consulting Machine Learning Data Science 144

CIO Business Intelligence

MAY 17, 2022

The company is on a mission to revolutionize the banking industry through technology and data and serves as a model for harnessing the power of data for growth. . Around 2015, Capital One went to AWS re:Invent and set forth our aspirational goal to modernize our entire technology infrastructure. How did you cope with this change?

ROI 52

ROI Risk Marketing Modeling 52

CIO Business Intelligence

APRIL 5, 2023

Shurts was CIO for Cadbury, Supervalu, and then for food distribution giant Sysco when he was appointed to his first board, for Con-Way Freight and Trucking, then a $5 billion public company, in 2015. Rather than talk about ERP programs or technology spend, you are better off talking about how technology disrupts business models.

Finance 52

Finance Digital Transformation Strategy Technology 52

Insight

JUNE 10, 2019

Photo by Wayne Chan on Unsplash The race to the future I don’t know about you, but I distinctly remember a promise from 1989 that flying cars would be commonplace by 2015. Transfer learning applied to ResNet50 CNN Training results The final accuracy of the model, when tested on a subset of the training data, was 97%.

Breakeven 69

Breakeven Modeling Testing Marketing 69

datapine

DECEMBER 28, 2021

To make sure the reliability is high, there are various techniques to perform – the first of them being the control tests, which should have similar results when reproducing an experiment in similar conditions. 29, 2015, Republicans from the U.S. 1) Examples of misleading statistics in the media and politics.

Statistics 363

Statistics Advertising Visualization Data mining 363

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Using ML models to search more effectively brought the search space down to 102—which can run on modest hardware. For details, see their SIGMOD 2015 paper where Michael Armbrust & co. Model-Driven Data Queries.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Sisense

FEBRUARY 24, 2021

But that number rose sharply afterwards, with the team noting there were over 1,000 people in this role by 2015. Challenge: CDOs are looking for an analytics solution that is flexible enough for the highly technical modeling that data analysts and engineers need while also being accessible for the business analyst.

Data Warehouse 69

Data Warehouse Big Data Metrics Visualization 69

AWS Big Data

OCTOBER 23, 2023

Before you run these workloads, most customers run SQL queries to interactively extract, filter, join, and aggregate data into a shape that can be used for decision-making, model training, or inference. This is a simplified model where we don’t need to use AWS Lake Formation data sharing. About the Authors Pathik Shah is a Sr.

Data Lake 104

Data Lake Visualization Optimization Interactive 104

bridgei2i

AUGUST 12, 2021

It was first presented by Ross Girshick, Shaoqing Ren, Kaiming He, and Jian Sun in 2015.[ Faster R-CNN is a single-stage model that is trained end-to-end. A note to the reader – a tried and tested Object Detection architecture gave this team promising results. Image Source - [link].

Testing 52

Testing ROI Enterprise IT 52

AWS Big Data

FEBRUARY 1, 2023

Storage and redundancy – Due to the heterogeneous data stores and models, it was challenging to store the different datasets from various business stakeholder teams. This will make launching and testing models simpler. The response times for these data sources are critical to our key stakeholders.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

Ontotext

FEBRUARY 2, 2021

Attempts to manipulate share prices by using social media to spread false or misleading information about stocks lead to the SEC’s Investor Alert of 2015, warning institutional investors about the possible impact of social media. However, it is difficult to map the r/WallStreetBets events to the classic financial fraud models.

Marketing 59

Marketing Measurement Cost-Benefit Risk 59

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Rita Sallam

APRIL 2, 2023

Interpretation of our machine learning model suggests buying $50k in building coverage and $100k in warehouse content coverage, as the latter significantly boosts predicted flood claim payouts. When exploring river and coastal flooding for the USA on a city level, river flooding area had a drastic increase starting from 2015.

Optimization 52

Optimization Machine Learning Insurance Risk 52

The Unofficial Google Data Science Blog

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. Structure of a geo experiment A typical geo experiment consists of two distinct time periods: pretest and test. After the test period finishes, the campaigns in the treatment group are reset to their original configurations.

Advertising 42

Advertising Testing Sales Statistics 42

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.

KDD 40

KDD Testing Machine Learning Modeling 40

BizAcuity

MAY 10, 2022

Amazon strategically went with the pricing model of ‘on-demand’, allowing developers to pay only as-per their computational needs. 2015: Google announces Google Kubernetes Engine for the cloud. AWS rolls out SageMaker, designed to build, train, test and deploy machine learning (ML) models. Google releases Kubernetes.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

Smart Data Collective

MARCH 22, 2019

As a comparison, the 2015 expenditure of robotics was only $71 bn. The RaaS model has recently become a common occurrence in the agro sector. Test the suggestions in a single department and scale the implemented technologies factory-wide, if successful. Assessing the current situation in the manufacturing unit. Conclusion.

Manufacturing 46

Manufacturing Cost-Benefit Enterprise Technology 46

Darkhorse

NOVEMBER 23, 2016

We get that sense of motion for geographically close states, but the visual doesn’t match our mental model of migration. This finally gives us a visual that matches our mental model: people moving over the period from one state to another. We tested out versions with net migration. Their migration is spread over the year.

Visualization 40

Visualization Interactive Testing Modeling 40

O'Reilly on Data

DECEMBER 9, 2019

Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment. For example, if a stock has a beta of 1.4

Statistics 27

Statistics Uncertainty Risk Marketing 27

The Unofficial Google Data Science Blog

JUNE 30, 2016

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. The choice of space $cal F$ (sometimes called the model ) and loss function $L$ explicitly defines the estimation problem. the curse of dimensionality).

Statistics 44

Statistics Optimization Modeling Experimentation 44