IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

CIO Business Intelligence

AUGUST 15, 2023

And it yields multiple business metric improvements, such as limiting surplus inventory. An interactive dashboard with mobile capabilities enables team members to view satellite health, each tagged asset with real-time location at the specified site, and heatmap data collected over a specified time range.

IT 98

IT Manufacturing IoT Cost-Benefit 98

DataRobot Blog

DECEMBER 1, 2022

Artificial intelligence (AI) can help improve the response rate on your coupon offers by letting you consider the unique characteristics and wide array of data collected online and offline of each customer and presenting them with the most attractive offers. Training and Testing Different AI Models. Automate Feature Engineering.

Machine Learning 52

Machine Learning Statistics Marketing Sales 52

IBM Big Data Hub

JULY 5, 2023

Today, asset management software helps companies maintain the most important information about their assets—such as condition, maintenance and repair history, location, licensing and performance metrics—more accurately and efficiently. What follows are some asset lifecycle management best practices that companies rely on.

Strategy 52

Strategy Management IoT Cost-Benefit 52

IBM Big Data Hub

AUGUST 28, 2023

Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., The data collection process should be tailored to the specific objectives of the analysis.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Domino Data Lab

OCTOBER 23, 2019

One of the things this data set doesn’t have, however, is a single metric to rank both offensive and defensive performance in a single statistic. To get this, we will need to combine this data set with other sources from ESPN and the NBA. Next, the following code constructs a URL that has the data range and username.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

AUGUST 1, 2021

Intrinsic methods – this technique is based on ANNs that have been designed to output an explanation alongside the standard prediction. Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Jet Global

MAY 1, 2023

As a result, end users can better view shared metrics (backed by accurate data), which ultimately drives performance. Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. Let them do what they want outside of the application.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52