Architecture Business Intelligence Hardware 
TechCrunch
MARCH 9, 2022
As organizations continue to build out their digital architecture, a new category of enterprise software has emerged to help them manage that process. “Enterprise architecture today is very much about the scaffolding in the organization,” he said.
Altexsoft
AUGUST 12, 2020
This article describes IoT through its architecture, layer to layer. Before we go any further, it’s worth pointing out that there is no single, agreed-upon IoT architecture. It varies in complexity and number of architectural layers depending on a particular business task. Major IoT building blocks and layers.
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CIO
JANUARY 20, 2023
Some are relying on outmoded legacy hardware systems. Most have been so drawn to the excitement of AI software tools that they missed out on selecting the right hardware. 2] Foundational considerations include compute power, memory architecture as well as data processing, storage, and security.
Altexsoft
APRIL 23, 2019
The answer is business intelligence. In this article, we will discuss the actual steps of bringing business intelligence into your existing corporate infrastructure. You will learn how to set up a business intelligence strategy and integrate tools into your company workflow. What is business intelligence?
Altexsoft
DECEMBER 17, 2019
In the business sphere, a certain area of technology aims at helping people make the right decisions, by supporting them with the right data. This field is called business intelligence or BI. Business intelligence includes multiple hardware and software units that serve the same idea: take data and show it to the right people.
CIO
OCTOBER 10, 2024
“We established the IT, Cybersecurity, and Digital Transformation departments, built the center’s IT infrastructure and data centers, and developed several critical systems like ERP, CRM, and Business Intelligence (BI),” he notes. Cloud technology has also played a crucial role in SAAC’s IT infrastructure.
CIO
JANUARY 24, 2024
Deep learning AI: A rising workhorse Deep learning AI uses the same neural network architecture as generative AI, but can’t understand context, write poems or create drawings. Training a GPT4-scale, trillion-parameter model takes billions of dollars in supercomputing hardware, months of time, and valuable data science talent.
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