Artificial Inteligence Business Analytics Compliance IoT 
Gorilla Logic
MAY 19, 2021
Monetize data with technologies such as artificial intelligence (AI), machine learning (ML), blockchain, advanced data analytics , and more. Create value from the Internet of Things (IoT) and connected enterprise. Some of the most common include cloud, IoT, big data, AI/ML, mobile, and more.
Datavail
APRIL 6, 2021
The term XaaS (“anything as a service”) is shorthand for the proliferation of cloud services in recent years—everything from databases and artificial intelligence to unified communications and disaster recovery is now available from your choice of cloud provider. on-premises is arriving in December 2021. Read This Next.
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Trigent
AUGUST 23, 2023
In the wake of hyper-digitization, manufacturers are leveraging advanced technologies like RPA, IoT, and AI to make their processes more efficient. The requirement of each manufacturing business is different based on industry & target customers, but many of their back-end processes are similar.
Kaseya
MAY 13, 2021
Common cloud functionalities offered by AWS that can help businesses scale and grow include: Networking and content delivery Analytics Migration Database storage Compute power Developer tools Security, identity and compliance Artificial intelligence Customer engagement Internet of Things Desktop and app streaming.
Sunflower Lab
APRIL 2, 2024
Use Cases of Power BI Predictive Analytics in Different Industries Use Case: Predictive Maintenance in Healthcare Business Scenario: A hospital aims to reduce equipment downtime and maintenance costs by implementing predictive maintenance.
Cloudera
MAY 12, 2021
Use cases could include but are not limited to: predictive maintenance, log data pipeline optimization, connected vehicles, industrial IoT, fraud detection, patient monitoring, network monitoring, and more. Data for Enterprise AI: E xperian BIS — Improving the accuracy of commercial data aggregation with data science and machine learning.
Altexsoft
FEBRUARY 8, 2021
To support the planning process, predictive analytics and machine learning (ML) techniques can be implemented. We have previously described demand forecasting methods and the role of machine learning solutions in a dedicated article. Today, consumers’ preferences are changing momentarily and often chaotically.
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