Domino Data Lab

MARCH 11, 2021

Here are several key considerations you should take into account when selecting a machine learning framework for your project. When you start your search for a machine learning framework, ask these three questions: Will you use the framework for deep learning or classic machine learning algorithms?

Machine Learning 143

Machine Learning Deep Learning Insurance Modeling 143

bridgei2i

OCTOBER 7, 2020

The unprecedented circumstances surrounding pandemic have increased awareness around insurable risks across different financial losses that can be covered as everyone in the world have faced financial losses like unemployment, travel cancellation, health issues, business interruptions, etc. The way ahead for insurers.

Insurance 52

Insurance Sales Strategy Digital Transformation 52

Cloudera

SEPTEMBER 11, 2020

48% of manufacturing companies surveyed recently said they’re moving toward greater IoT integration which will allow them to better respond to real-time data and feedback and monitor broader, external datasets that they hadn’t traditionally been using. Supply-side. Automation opportunities.

Manufacturing 95

Manufacturing Forecasting Digital Transformation Sales 95

Cloudera

SEPTEMBER 12, 2018

Enterprise Machine Learning: . AbbVie’s platform uses analytics and machine learning, including natural language processing, deep learning, and unsupervised learning, to proactively identify issues and opportunities. Brian Buntz , Content Director, Iot Institute, Informa, @brian_buntz. Technical Impact.

Machine Learning 46

Machine Learning Big Data Data Science Data Warehouse 46

AWS Big Data

MAY 16, 2024

H3 can also help create location-based profiling features for predictive machine learning (ML) models such as risk-mitigation models. This insight aids in predicting future demand and facilitates operations-related decisions.

Data Warehouse 109

Data Warehouse Visualization Cost-Benefit Optimization 109

Smart Data Collective

MAY 17, 2021

A critical component of smarter data-driven operations is commercial IoT or IIoT, which allows for consistent and instantaneous fleet tracking. The global IoT fleet management market is expected to reach $17.5 A mission-critical task like maintenance can be relegated to proactive measures thanks to a steady flow of performance data.

Big Data 100

Big Data Cost-Benefit IoT Machine Learning 100

BizAcuity

MARCH 31, 2022

85% of AI (marketing) projects fail due to risk, confusion, and lack of upskilling among marketing teams.(Source: Not just banking and financial services, but many organizations use big data and AI to forecast revenue, exchange rates, cryptocurrencies and certain macroeconomic variables for hedging purposes and risk management.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

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 Modeling Data-driven 59

IBM Big Data Hub

MAY 17, 2023

Additionally, DaaS has the potential to leverage smart technologies such as IoT, sensors, 5G, edge analytics and machine learning to improve the monitoring and visualization of assets operations. DaaS uses built-in deep learning models that learn by analyzing images and video streams for classification.

Data Collection 77

Data Collection Visualization Data-driven Unstructured Data 77

Domino Data Lab

MARCH 3, 2019

O’Reilly Media had an earlier survey about deep learning tools which showed the top three frameworks to be TensorFlow (61% of all respondents), Keras (25%), and PyTorch (20%)—and note that Keras in this case is likely used as an abstraction layer atop TensorFlow. The data types used in deep learning are interesting.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. Deep learning,” for example, fell year over year to No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

CIO Business Intelligence

JANUARY 20, 2023

Topping the list of executive priorities for 2023—a year heralded by escalating economic woes and climate risks—is the need for data driven insights to propel efficiency, resiliency, and other key initiatives. Many companies have been experimenting with advanced analytics and artificial intelligence (AI) to fill this need.

Analytics 137

Analytics Key Performance Indicator Unstructured Data IoT 137

O'Reilly on Data

MARCH 24, 2020

John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing. This need will grow as smart devices, IoT, voice assistants, drones, and augmented and virtual reality become more prevalent.

Machine Learning 343

Machine Learning Statistics Data Quality Modeling 343