CIO
NOVEMBER 23, 2022
Enterprise architecture definition Enterprise architecture (EA) is the practice of analyzing, designing, planning, and implementing enterprise analysis to successfully execute on business strategies. Making it easier to evaluate existing architecture against long-term goals.
CIO
MARCH 6, 2024
continues to roll out, the internet of things (IoT) is expanding, and manufacturing organizations are using the latest technologies to scale. Marrying machine learning with crowdsourced telemetry and passive identification technology enables organizations to rapidly assess and score risk for everything and everyone that you can now see.
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Altexsoft
MAY 12, 2022
Today, we have AI and machine learning to extract insights, inaudible to human beings, from speech, voices, snoring, music, industrial and traffic noise, and other types of acoustic signals. It’s vital for understanding surroundings in IoT applications. Source: Audio Singal Processing for Machine Learning.
Confluent
FEBRUARY 6, 2019
Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs.
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. Don’t use generative AI for a problem that classical machine learning has already solved. Learn more. Learn more. [1]
O'Reilly Media - Ideas
FEBRUARY 4, 2019
In a recent survey , we explored how companies were adjusting to the growing importance of machine learning and analytics, while also preparing for the explosion in the number of data sources. As interest in machine learning (ML) and AI grow, organizations are realizing that model building is but one aspect they need to plan for.
CIO
AUGUST 8, 2023
Traditional, centralized computing architectures cannot deliver the speed and reliability required for critical frontline tasks. Advanced analytics: Edge computing empowers frontline workers with machine learning algorithms for predictive and prescriptive recommendations for more rapid task execution.
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