IBM Big Data Hub

APRIL 28, 2020

During these times of uncertainty, all companies are being stressed in new ways; supply chains are being halted with employee sickness, retail store doors are closed to encourage social distancing, and health care facilities are overwhelmed by patient demand.

Uncertainty 82

Uncertainty Data Science Risk 82

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. He also recommends that PMs refrain from “endless UI changes” on ML projects before the product is put before users because “seemingly small UI changes may result in significant back end ML engineering work” that may put the overall project at risk.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

O'Reilly on Data

JANUARY 22, 2019

They trade the markets using quantitative models based on non-financial theories such as information theory, data science, and machine learning. Whether financial models are based on academic theories or empirical data mining strategies, they are all subject to the trinity of modeling errors explained below.

Modeling 134

Modeling Finance Uncertainty Data mining 134

CIO Business Intelligence

APRIL 28, 2022

Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission. Particularly in Asia Pacific , revenues for big data and analytics solutions providers hit US$22.6bn in 2020 , with financial services companies ranking among their biggest clients.

Analytics 88

Analytics Insurance Big Data Uncertainty 88

CIO Business Intelligence

MARCH 25, 2022

Two years of pandemic uncertainty and escalating business risk have sharpened the focus of corporate boards on a technology trend once dismissed as just another IT buzzword. What’s great in what I see today is how much digital transformation is embedded into the fabric of the business strategy.

Digital Transformation 106

Digital Transformation Uncertainty Machine Learning Strategy 106

IBM Big Data Hub

APRIL 18, 2024

Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. Most importantly, no matter the strength of AI (weak or strong), data scientists, AI engineers, computer scientists and ML specialists are essential for developing and deploying these systems.

Cost-Benefit 96

Cost-Benefit Modeling Manufacturing Interactive 96

Cloudera

OCTOBER 27, 2020

Our goal is to take the incredible data science and machine learning research developments we see emerging from academia and large industrial labs, and bridge the gap to products and processes that are useful to practitioners working across industries. At Cloudera Fast Forward we work to make the recently possible useful. And beyond.

Deep Learning 59

Deep Learning Machine Learning Forecasting Uncertainty 59

bridgei2i

JUNE 3, 2020

Consumers feel threatened by the prolonged uncertainty, not having had to deal with anything like it, in their lives. Demand for minimal physical interaction/low human touch products: Due to the risk of infection, customers want to pick up products with minimal human contact.

Manufacturing 52

Manufacturing Forecasting Uncertainty Digital Transformation 52

Peter James Thomas

JUNE 20, 2019

The above chart compares monthly searches for Business Process Reengineering (including its arguable rebranding as Business Transformation ) and monthly searches for Data Science between 2004 and 2019. Here we come back to the upward trend in searches for Data Science. And a more competent Chief Risk Officer. .

Cost-Benefit 55

Cost-Benefit Data Warehouse Consulting Data Science 55

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. A single model may also not shed light on the uncertainty range we actually face.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

The Unofficial Google Data Science Blog

APRIL 23, 2024

Crucially, it takes into account the uncertainty inherent in our experiments. Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. It is a big picture approach, worthy of your consideration.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data. Such a model risks conflating important aspects, notably the growth trend, with other less critical aspects.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Domino Data Lab

JUNE 2, 2019

Co-chair Paco Nathan provides highlights of Rev 2 , a data science leaders summit. We held Rev 2 May 23-24 in NYC, as the place where “data science leaders and their teams come to learn from each other.” If you lead a data science team/org, DM me and I’ll send you an invite to data-head.slack.com ”.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. and even set their risk tolerance. Input page for ReelRisk.

Risk 67

Risk ROI Modeling Metrics 67

Cloudera

NOVEMBER 18, 2021

By adopting a custom developed application based on the Cloudera ecosystem, Carrefour has combined the legacy systems into one platform which provides access to customer data in a single data lake. Data for Good. Learn more about the Cloudera Data Impact Awards and see past winners!

Data Lake 73

Data Lake Cost-Benefit Digital Transformation Risk 73

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. For example, imagine a fantasy football site is considering displaying advanced player statistics.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

AUGUST 27, 2019

The bucketing method also changes the importance sampling to a stratified sampling setting, and allows us to use binomial confidence intervals to estimate the uncertainty of our estimate (more on that later). High Risk 10% 5% 33.3% Whether or not we borrow strength from other scores also impacts the estimation. How Many Strata?

Metrics 98

Metrics Statistics Uncertainty Optimization 98

CIO Business Intelligence

JUNE 15, 2022

A recent RPA project at Voya successfully reduced a tax calculation process time by 80%, reducing the risk of human error, to boot. Blue Prism’s internal Center of Excellence focuses on automating as much work as possible and moving toward self-service. Much of IT work is repetitive. Consider a high-low strategy.

IT 145

IT Digital Transformation Technology Strategy 145

Cloudera

OCTOBER 3, 2018

Cloudera offers the Cloudera Data Science Workbench (CDSW) and Workload Experience Manager (Workload XM). In the meantime, each of us also has unique product offerings. Hortonworks offers its Hortonworks DataFlow, or HDF, product for streaming and IoT workloads. Forward-Looking Statements.

Uncertainty 75

Uncertainty IoT Risk Reporting 75

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Domino Data Lab

JUNE 23, 2019

If you have a user facing product, the data that you had when you prototype the model may be very different from what you actually have in production. This really rewards companies with an experimental culture where they can take intelligent risks and they’re comfortable with those uncertainties.

Management 110

Management Machine Learning Experimentation Metrics 110

Peter James Thomas

AUGUST 12, 2018

A clear parallel would be credit risk in Retail Banking, but something as simple as an estimate of potentially delinquent debtors is an inherently statistical figure (albeit one that may not depend on the output of a statistical model). However such estimates appear in a number of industries, sometimes explicitly, sometimes implicitly.

Metrics 49

Metrics Statistics Data Quality Measurement 49

The Unofficial Google Data Science Blog

MAY 31, 2016

Further, there is the risk that the increased ad spend will be less productive due to diminishing returns (e.g., In practice, the focus of the team is however on the estimate of $beta_2$, not to forget about the uncertainty around this estimate: the confidence interval half-width was estimated to be 0.27.

Advertising 42

Advertising Testing Sales Statistics 42

CIO Business Intelligence

FEBRUARY 14, 2024

While the past few years have left us with a business landscape scarred by the impact of economic and geopolitical uncertainties, the current AI movement has become a rocket ship for significant transformative changes set to accelerate new opportunities.

Data-driven 72

Data-driven Uncertainty Enterprise Business Objectives 72