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

CIO Business Intelligence

NOVEMBER 29, 2022

AWS launched Amazon Connect in 2017 in an effort to offer a low-cost, high-value alternative to traditional customer service software suites. Amazon Connect uses a pay-for-what-you-use model, and no upfront payments or long-term commitments are required to sign up for the service.

Machine Learning 91

Machine Learning Forecasting Recreation/Entertainment Metrics 91

Smart Data Collective

DECEMBER 9, 2020

In 2017, the university forged a partnership with Microsoft and the city of Bellevue. The goal is to develop predictive analytics models that will be able to recommend changes to prevent such accidents from occurring in the first place. New machine learning initiatives offer promising opportunities to lower car accidents.

Machine Learning 141

Machine Learning Risk Insurance Predictive Analytics 141

datapine

MAY 11, 2022

While pie charts have received a bad rep in recent years, we feel that they form a useful visualization tool that serves up important metrics in an easy-to-follow format. Retail analytics tools allow you to visualize relevant metrics in interactive bar charts such as the one displayed below. c) Pie charts. d) Gauge charts.

Visualization 252

Visualization Dashboards Key Performance Indicator Sales 252

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. Adrian Weller (2017-07-29). “

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

AWS Big Data

AUGUST 7, 2023

Amazon Redshift ML makes it easy for data analysts and database developers to create, train, and apply machine learning (ML) models using familiar SQL commands in Amazon Redshift. Simply use SQL statements to create and train SageMaker ML models using your Redshift data and then use these models to make predictions.

Forecasting 86

Forecasting Data Warehouse Machine Learning Modeling 86

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. We will follow the example of Janson and Olsson , and start from this generalized definition of the metric, which they call iota.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

CIO Business Intelligence

JANUARY 23, 2023

There’s greater appreciation from business leaders that technology is what’s driving efficiencies, disrupting products, and underpinning new revenue models. They were also instrumental in developing models that optimized freight costs and logistics routes — all critical pain points during and post pandemic.

Digital Transformation 145

Digital Transformation Technology Management Insurance 145

CIO Business Intelligence

JULY 20, 2022

Yet in the cybersecurity field, women only account for about one quarter (24%) of the overall workforce, albeit, up from 11% in 2017, according to the ISC2 Women in Cybersecurity report. A 2020 World Economic Forum report found that women make up a similar percentage (26%) of data and AI positions in the workforce. “We

IT 128

IT Software Cost-Benefit Strategy 128

CIO Business Intelligence

MAY 16, 2023

higher [in 2022] than in 2017.” AI Opportunities Generative AI is the basis for sophisticated AI models such as ChatGPT and Dall-E. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data.

Advertising 80

Advertising Recreation/Entertainment Experimentation Enterprise 80

Cloudera

APRIL 7, 2021

A 2017 PWC survey of over 100 global insurance company CEOs revealed that the number one objective of these CEOs was to “get closer to their customers and to better understand their evolving needs.” Changing Business Models. Lack of Investment and Management Focus. Data — Too much and Too little. The Hunt for Talent.

Insurance 58

Insurance Digital Transformation Internet of Things IoT 58

datapine

NOVEMBER 27, 2019

Marketers determine customer responses or purchases and set up cross-sell opportunities, whereas bankers use it to generate a credit score – the number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) from sklearn.neighbors import NearestNeighbors from random import randrange. return synthetic.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Data Science 101

MAY 6, 2019

I think in physics one of the things that attracted me most to the field that I studied, which was particle physics, was the ability to leverage computer science mathematical modeling and data visualization to solve big questions. And what kind of transformation do you need to apply to the data set to enable modeling?

Data Science 98

Data Science Modeling Machine Learning Testing 98

Smarten

JANUARY 7, 2020

Every business understands the value of objective metrics and accurate analysis and the modern BI environment is designed to support these goals at every level of the organization. How is Modern BI Different From Traditional BI? Modern BI supports collaboration, while providing appropriate data governance and data security.

IT 52

IT Business Intelligence ROI Reporting 52

BizAcuity

APRIL 28, 2019

is delinquent as of June 30th, 2017. By clubbing various techniques like data mining, machine learning, artificial intelligence and statistical modelling, it makes predictions about events in the future. Also, we noticed that, earlier, the data consisted of only a few metrics, based on which the team classified its customers.

Predictive Analytics 98

Predictive Analytics Analytics Big Data Data mining 98

Smarten

NOVEMBER 19, 2018

Predictive analytics employs various analytical and modeling techniques, leveraging historical data and business results to identify crucial relationships, opportunities and risks so that business managers can more accurately predict growth, and competitive and market changes and identify trends and patterns. What is Predictive Analytics?

Business Objectives 40

Business Objectives Predictive Analytics Analytics Forecasting 40

Peter James Thomas

AUGUST 12, 2018

Pertinence and fidelity of metrics developed from Data. Metrics are seldom reliant on just one data element, but are often rather combinations. There are often compromises to be made in defining metrics. Again see Using BI to drive improvements in data quality for further details. Some of these are based on the data available.

Metrics 49

Metrics Statistics Data Quality Measurement 49

Domino Data Lab

FEBRUARY 10, 2019

Metrics can also increase a lot in the early days of a project and then suddenly hit a wall. I once ran a Kaggle competition where thousands of people competed around the world to model my data. It’s possible, even common, for a week spent on modeling data to result in no improvement whatsoever. We might expect that.

Machine Learning 63

Machine Learning Modeling Deep Learning Metrics 63

Cloudera

MAY 18, 2021

In this article, we will be using synthetic market data generated by an agent-based model (ABM) developed by Simudyne. Rather than a top-down approach, ABMs model autonomous actors (or agents) within a complex system — for example, different kinds of buyers and sellers in financial markets. Intraday VaR. Citations. [1]

Risk 96

Risk Marketing Risk Management Data-driven 96

Smarten

NOVEMBER 22, 2017

Self-Serve Advanced Analytics provides a roadmap to guide you through a maze of organizational data with tools and models that will bring together data from disparate sources and enable sophisticated yet simple analytics. These tools allow the enterprise to establish and monitor key metrics and to objectively assess results.

Analytics 40

Analytics IT Business Intelligence Visualization 40

Occam's Razor

JUNE 7, 2017

And, less than 1% engagement of any kind from that less than 1% reached (identified using the best social media metrics: Conversation Rate, Amplification Rate, Applause Rate ). To give you some context as to how insanely lame these numbers are, Expedia.com received 59,400,000 Visits in May 2017. 3/6,462,977. Unaided Brand Recall.

Advertising 162

Advertising Marketing B2B ROI 162

Domino Data Lab

SEPTEMBER 9, 2019

We have configured the default Compute Environment in Domino to include all of the packages, libraries, models, and data you’ll need for this tutorial. That nlp variable is now your gateway to all things spaCy and loaded with the en_core_web_sm small model for English. Getting Started. for a, b in pairs:?. print(a, b, lic[a].similarity(lic[b])).

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

Alation

AUGUST 26, 2021

HBR Review May/June 2017. These future-oriented models are used to make predictions. A common BI application is the BI dashboard, which displays key metrics so that leaders have a “big picture view” to inform wise goals and decisions. AI and ML are used in concert to predict possible events and model outcomes.

Metadata 64

Metadata Data Governance Dashboards Software 64

Occam's Razor

JANUARY 8, 2018

Media mix modeling. If you are a reader of my newsletter, The Marketing < > Analytics Intersect , you’ve seen me apply it to metrics (last TMAI was on Bounce Rate), reports, frameworks and more. In 2017, they've twice replaced the company's Chief Revenue Officer. Multi-petabyte data warehouses. But, no biggie.

Sales 133

Sales Predictive Analytics Insurance Data Warehouse 133

Insight

OCTOBER 25, 2019

My client also specified that CAD model files of the T-LESS dataset be used for this project, and that one object per class be reserved for testing (Objects 4, 8, 12, 18, 23, 30). Three views for Object 12: CAD model as visualized by Microsoft’s Print 3D software (left). Constructing the tasks used in my model.

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

Jet Global

MARCH 2, 2022

At a minimum , companies should provide information relating to their business model, the outcome of their policies, the principal risks they face linked to their business operations, and other non-financial key performance indicators relevant to their business.

Reporting 59

Reporting Recreation/Entertainment Risk Insurance 59

O'Reilly on Data

JANUARY 25, 2021

Our data shows that Chef and Puppet peaked in 2017, when Kubernetes started an almost exponential growth spurt, as Figure 4 shows. Models are neither code nor data, and we don’t have adequate tools for versioning models either (though tools like MLflow are a start).

Machine Learning 362

Machine Learning Software Testing Technology 362

Domino Data Lab

OCTOBER 23, 2019

This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. This means covering details like setting up your environment, deployment, and monitoring, in addition to creating models on clean data. Phrasing the Problem. questions to ask. Gathering the Data. In Figure 6.1,

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

FEBRUARY 4, 2019

People who attended JupyterCon 2017–2018 can attest, an “industry poster session” includes an open bar, catered hors d’oeuvres, lots of mingling … to paraphrase feedback from JupyterCon, “As a tech person, would I get up extra early to meet strangers for coffee at 8:00 am? Problems with training ML models efficiently.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Occam's Razor

JULY 13, 2017

5: What-if Analysis Models. Oh, or your main traffic sources and the visitor acquisition metrics? allow for smart elements like modeling. Strategy 5: What-if Analysis Models. Why not build in a model where the decision maker can change Conversion Rates, to see the impact on Revenue? 2: If Complex, Focus!

Visualization 124

Visualization Strategy Interactive Statistics 124

Smarten

APRIL 24, 2019

Augmented Analytics allows organizations to integrate data from numerous data sources and to use that data to analyze and display results in a clear manner so the business can make unbiased decisions and establish objective metrics. Users can compare results against plans and forecasts.

Business Intelligence 0

Business Intelligence Analytics IT Metrics 0