Architecture Artificial Inteligence Scalability 
CIO
NOVEMBER 27, 2024
Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.
CIO
JANUARY 30, 2025
From data masking technologies that ensure unparalleled privacy to cloud-native innovations driving scalability, these trends highlight how enterprises can balance innovation with accountability. With machine learning, these processes can be refined over time and anomalies can be predicted before they arise.
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CIO
DECEMBER 20, 2024
Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Ensure security and access controls.
CIO
MARCH 20, 2025
Generative and agentic artificial intelligence (AI) are paving the way for this evolution. AI practitioners and industry leaders discussed these trends, shared best practices, and provided real-world use cases during EXLs recent virtual event, AI in Action: Driving the Shift to Scalable AI. The EXLerate.AI
AWS Machine Learning - AI
APRIL 9, 2025
Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements.
Dzone - DevOps
MARCH 28, 2025
With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications.
Openxcell
JANUARY 20, 2025
LLM or large language models are deep learning models trained on vast amounts of linguistic data so they understand and respond in natural language (human-like texts). The inner transformer architecture comprises a bunch of neural networks in the form of an encoder and a decoder.
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