DataRobot Blog

MARCH 9, 2022

The Amazon Product Reviews Dataset provides over 142 million Amazon product reviews with their associated metadata, allowing machine learning practitioners to train sentiment models using product ratings as a proxy for the sentiment label. It provides 1.6 Sentiment analysis, a baseline method.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

CIO Business Intelligence

AUGUST 2, 2022

Reyes has been with AES since 2007, working his way up the organization ladder from an SAP integration lead in Buenos Aires to application security manager, IT project director, and director of digital transformation today.

Data Quality 97

Data Quality Digital Transformation Machine Learning Predictive Analytics 97

Decision Management Solutions

AUGUST 18, 2020

DDPs accomplish this by providing a suite of capabilities that enable business subject-matter experts to define decision logic, incorporate data-driven decision intelligence technologies such as machine learning (ML), govern change, and deploy digital decisions within business applications. Does that change the offers we make?

Machine Learning 88

Machine Learning Data-driven Predictive Analytics Descriptive Analytics 88

Domino Data Lab

SEPTEMBER 25, 2019

Yet, because the last event affects the current event and ordering of events matter, we are obligated to use more specialized tools as compared to plain regression or classification machine learning algorithms. class(attClose) [1] "xts" "zoo" > head(attClose) T.Close 2007-01-03 34.95 2007-01-04 34.50 2007-01-05 33.96

Forecasting 87

Forecasting Modeling Statistics Optimization 87

CIO Business Intelligence

APRIL 28, 2022

auxmoney began as a peer-to-peer lender in 2007, with the mission of improving access to credit and promoting financial inclusion. Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission.

Analytics 98

Analytics Insurance Big Data Uncertainty 98

CIO Business Intelligence

MAY 15, 2023

The technology made its debut at the Australian Open in 2003 and Wimbledon in 2007, and it provides the foundation for electronic line calling for the sport. Digital Transformation, Machine Learning, Machine Vision We’re seeing a big amount of development across AI-based camera tracking systems.

Visualization 106

Visualization Dashboards Digital Transformation Machine Learning 106

Cloudera

AUGUST 8, 2022

The general availability covers Iceberg running within some of the key data services in CDP, including Cloudera Data Warehouse ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). Cloudera Machine Learning . 2 2007 7453215. Cloudera Data Engineering (Spark 3) with Airflow enabled.

Snapshot 94

Snapshot Data Warehouse Machine Learning Cost-Benefit 94

Ontotext

MAY 26, 2021

One of the main uses of the Gold Standard is to train AI systems to identify the patterns in various types of data with the help of machine learning (ML) algorithms. In other words, by giving them plenty of data to learn from, we can “teach” AI systems to automatically identify such patterns. Gold Standard takeaways.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

AWS Big Data

JULY 26, 2023

AWS offers AWS Glue to help you integrate your data from multiple sources on serverless infrastructure for analysis, machine learning (ML), and application development. Ertel, Allen| |1916-09-01| null|2007-11-24| Minish| male| Joseph|018247d0-2961-423.|[{M000796, One of the most common options is the notebook. .|

Data Integration 98

Data Integration Interactive Machine Learning Big Data 98

Sisense

MAY 12, 2019

DevOps first came about in 2007-2008 to fix problems in the software industry and bring with it continuous improvement and greater efficiencies. Predictive analytics uses AI (Augmented Intelligence) and ML (Machine Learning) to identify other patterns and relationships that might be hidden or harder to realize.

Analytics 79

Analytics Dashboards KPI Metrics 79

DataRobot

JUNE 10, 2021

If machine learning could contribute, this would allow for the faster invention of new compounds tailored for particular aromatic signatures. It was introduced in 1980 but open-sourced in 2007, which created its widespread use. The objective of the competition is to predict the smell or aromatic properties of a given molecule.

Metrics 52

Metrics Machine Learning Visualization Experimentation 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

The field of statistical machine learning provides a solution to this problem, allowing exploration of larger spaces. Model Stacking - Super Learner (SL) When using predictive power as a criterion, the question arises of how to select among the many prediction methods available in the statistical learning literature.

Statistics 45

Statistics Optimization Modeling Experimentation 45

Rita Sallam

JULY 22, 2016

Like when Oracle acquired Hyperion in March of 2007, which set of a series of acquisitions –SAP of Business Objects October, 2007 and then IBM of Cognos in November, 2007. Reeboks made it possible for aerobics classes to become main stream beyond its dancer beginnings. In BI we have had our seminal moments too.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

The Unofficial Google Data Science Blog

APRIL 19, 2021

Practical Reason #2: Model Modularity In complex machine learning systems, models depend on each other. This expected value can be helpful for simulating the impact of an experiment (does this increase expected clicks enough to merit running an actual experiment?) For these ML systems, calibration simplifies interaction.

Modeling 122

Modeling IT Metrics Testing 122

In(tegrate) the Clouds

OCTOBER 7, 2016

As the data visualization, big data, Hadoop, Spark and self-service hype gives way to IoT, AI and Machine Learning, I dug up an old parody post on the business intelligence market circa 2007-2009 when cloud analytics was just a disruptive idea. Here’s the post in it’s entirety (with apologies to Billy Joel).

Business Intelligence 40

Business Intelligence OLAP Marketing Scorecard 40

The Data Visualisation Catalogue

AUGUST 15, 2019

Back in 2007, Harvard argued (in a review) that the customer experience was starting to spread into other areas of business. So to solve this problem we have developed systems that combine traditional techniques with machine learning to create powerful visuals. It was no longer just about catering to their needs.

Visualization 50

Visualization Dashboards Interactive Marketing 50

BizAcuity

MAY 10, 2022

2007: Amazon launches SimpleDB, a non-relational (NoSQL) database that allows businesses to cheaply process vast amounts of data with minimal effort. Microsoft launches Azure ML Studio for machine learning capabilities on the cloud. AWS rolls out SageMaker, designed to build, train, test and deploy machine learning (ML) models.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

Cloudera

DECEMBER 11, 2017

Claudia Juech is the founding Executive Director of the Cloudera Foundation , which will use Cloudera’s expertise in data analytics and machine learning to change people’s lives for the better. Then they came for me—and there was no one left to speak for me.

Data-driven 40

Data-driven Strategy Reporting Data Processing 40

The Unofficial Google Data Science Blog

APRIL 23, 2024

Springer New York, 2007. [8] Journal of Machine Learning Research, 17(83):1–5, 2016. [23] A Library of Orthogonal Arrays. Accessed 2023-10-01. [7] Mukerjee and C.F.J. A Modern Theory of Factorial Design. Springer Series in Statsitics. Hammond, R.L. Keeney, and H. Smart Choices: A Practical Guide to Making Better Decisions.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

CIO Business Intelligence

JUNE 13, 2022

After all, XANTAS was founded in 2007 specifically to create software for data analysis software in hospitals – all based on SAP technology. “A But when you combine it with machine learning algorithms, you can do good things.”. Crossing at the green. Artificial intelligence is the next step,” Telle said. Data Management

Data Warehouse 98

Data Warehouse Manufacturing Software Machine Learning 98

Grooper

AUGUST 19, 2019

Applied analytics Business analytics Machine learning and data science. Machine Learning and Data Science. Machine learning (ML) centers around the learning process of computers. Machine Learning is King, Data Science the Heart. How Machine Learning is Used. Algorithms.

Analytics 54

Analytics Machine Learning Data Science Data-driven 54

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. Learning about learning. and dig into details about where science meets rhetoric in data science. Friction ensued.

Data Science 66

Data Science Experimentation Machine Learning Data-driven 66

The Unofficial Google Data Science Blog

JULY 18, 2023

2007): 77-89) Computing Krippendorff's Alpha-Reliability (Krippendorff, 2011) Cross-replication Reliability - An Empirical Approach to Interpreting Inter-rater Reliability (Wong et al., Educational and psychological measurement 20.1 Communication methods and measures 1.1 ACL-IJCNLP 2021) Data Excellence: Better Data for Batter AI (Aroyo, L.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

bridgei2i

OCTOBER 27, 2020

Intent Marketing has been around since 2007, but the big step forward came in the year 2015 when Google started capturing intent of the searcher. Most of the lead scoring methods are rule-based or machine learning-based using the information captured at the moment of lead identification. Intent Marketing.

Data Science 75

Data Science Sales Data-driven Marketing 75