Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal.

Data Science 110

Data Science Statistics Predictive Analytics Recreation/Entertainment 110

Smart Data Collective

MAY 17, 2021

All that performance data can be fed into a machine learning tool specifically designed to identify certain events, failures or obstacles. Predictive models, estimates and identified trends can all be sent to the project management team to speed up their decisions. That’s also where big data can step in and vastly expand ops.

Big Data 132

Big Data Cost-Benefit IoT Machine Learning 132

CIO Business Intelligence

MARCH 22, 2023

The new class often uses advanced techniques such as deep learning, natural language processing, and computer vision to analyze and extract insights from the data. It is often used to train machine learning models and protect sensitive data in healthcare and finance. The solution is also partially risk-free.

Machine Learning 105

Machine Learning Cost-Benefit Modeling Risk 105

IBM Big Data Hub

DECEMBER 20, 2023

For instance, if data scientists were building a model for tornado forecasting, the input variables might include date, location, temperature, wind flow patterns and more, and the output would be the actual tornado activity recorded for those days. temperature, salary).

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

IBM Big Data Hub

JULY 6, 2023

Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. An AI-based medical assessment platform analyzes medical records to determine a patient’s risk of stroke and predict treatment plan success rates.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

IBM Big Data Hub

AUGUST 28, 2023

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

IBM Big Data Hub

MAY 17, 2024

But as businesses around the globe rapidly adopt the technology to augment processes from merchandising to order management, there is some risk. These tools enable companies to proactively identify potential disruptions and mitigate risks. Generative AI’s impact on the social media landscape garners occasional bad press.

B2B 64

B2B Interactive Optimization Data-driven 64

Alation

APRIL 14, 2022

They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. These factors risk data originating in far-flung environments, where the data structures and semantics are not well understood or documented. It’s not a simple definition.

Metadata 59

Metadata Data Governance Data-driven Modeling 59

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection. Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation.

Machine Learning 48

Machine Learning Marketing Forecasting Modeling 48

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. deep learning) there is no guaranteed explainability.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

IBM Big Data Hub

FEBRUARY 14, 2024

Automotive With applications of AI, automotive manufacturers are able to more effectively predict and adjust production to respond to changes in supply and demand. They can streamline workflows to increase efficiency and reduce time-consuming tasks and the risk of error in production, support, procurement and other areas.

Cost-Benefit 82

Cost-Benefit Insurance Machine Learning Unstructured Data 82

O'Reilly on Data

SEPTEMBER 11, 2019

For example, even though ML and ML-related concepts —a related term, “ML models,” (No. Deep learning,” for example, fell year over year to No. But the database—or, more precisely, the data model —is no longer the sole or, arguably, the primary focus of data engineering. 40; it peaked at Strata NY 2018 at No.

IoT 23

IoT Big Data Data Warehouse Uncertainty 23

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