Artificial Inteligence Data Engineering Enterprise Scalability 
CIO
APRIL 10, 2024
Inferencing has emerged as among the most exciting aspects of generative AI large language models (LLMs). A quick explainer: In AI inferencing , organizations take a LLM that is pretrained to recognize relationships in large datasets and generate new content based on input, such as text or images.
CIO
DECEMBER 11, 2023
Both healthcare payers and providers remain cautious about how to use this latest version of artificial intelligence, and rightfully so. You have to balance the potential benefits of generative AI with significant, important operational issues, such as ensuring patient data privacy and complying with regulatory requirements.
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AWS Machine Learning - AI
JANUARY 26, 2024
Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. We then discuss how building on a secure foundation is essential for generative AI.
CIO
FEBRUARY 14, 2023
By George Trujillo, Principal Data Strategist, DataStax Increased operational efficiencies at airports. Investments in artificial intelligence are helping businesses to reduce costs, better serve customers, and gain competitive advantage in rapidly evolving markets. Instant reactions to fraudulent activities at banks.
d2iq
JANUARY 18, 2022
Security: Data privacy and security are often afterthoughts during the process of model creation but are critical in production. D2iQ is an AWS Containers Competency Partner , and D2iQ Kaptain is an enterprise Kubeflow product that enables organizations to develop and deploy machine learning workloads at scale.
Altexsoft
JUNE 29, 2021
In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. Google, in turn, uses the Google Neural Machine Translation (GNMT) system, powered by ML, reducing error rates by up to 60 percent. The role of a machine learning engineer in the data science team.
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. Impedance mismatch between data scientists, data engineers and production engineers.
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