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Overcome roadblocks to GenAI adoption and unlock ROI
GenAI deployments can fall prey to unrealistic goals, misguided pilots, job loss fears, hidden costs and lack of trust. Governance and workforce readiness are keys to success.
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Open source AI: What it means for enterprise innovation
Open source AI is transforming enterprise innovation with greater flexibility and control, but organizations must address governance, security and operational challenges to scale effectively.
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FinOps can manage AI computing costs, experts say
FinOps is no longer just about optimizing cloud spending. Hear from experts on what makes FinOps a desirable option to help businesses manage their AI costs.
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History of generative AI innovations spans 9 decades
Rapid GenAI advances are reshaping industries, sparking legal battles and driving embodied AI innovations, paving the way for transformative business processes and tools.
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AI Infrastructure Evaluate
Open source AI: What it means for enterprise innovation
Open source AI is transforming enterprise innovation with greater flexibility and control, but organizations must address governance, security and operational challenges to scale effectively.
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AI Technologies Manage
Managing drift in AI models and data
The training data and algorithms used to build AI models have a shelf life. Detecting and correcting model drift ensures that these systems stay accurate, relevant and useful.
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AI Platforms Evaluate
What Nvidia's $78B quarter tells you about enterprise AI
Nvidia's latest earnings reveal more than impressive revenue figures. They highlight the accelerating adoption of enterprise AI and the growing pressure on infrastructure capacity.
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ML Platforms Get Started
Regression in machine learning: A crash course for engineers
Regression in machine learning helps organizations forecast and make better decisions by revealing the relationships between variables. Learn how it's applied across industries.
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AI Business Strategies News
Overcome roadblocks to GenAI adoption and unlock ROI
GenAI deployments can fall prey to unrealistic goals, misguided pilots, job loss fears, hidden costs and lack of trust. Governance and workforce readiness are keys to success.
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Applications of AI Get Started
Time for AI: The 'too busy' problem is a software-age hangover
Higher education must prioritize enterprise AI as a strategic shift, not just tool buying. Learn how governance, alignment and a five-year plan can transform institutions.
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Processors
AWS + Intel Xeon6: Fastest Intel-Powered EC2 Yet
Power your workloads with AWS's new 8th generation EC2 instances, featuring custom Intel Xeon 6 processors - exclusively on AWS! The complete family of C8i, M8i, and R8i instances, along with their flexible counterparts (C8i-Flex, M8i-Flex, R8i-Flex), are now available.
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Artificial Intelligence
Kaspersky – Optimizing your SOC
Are you interested in developing your organization’s SOC or optimizing an existing one? Kaspersky shares its expertise in articles built on its vast experience building and managing successful SOCs.
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Articlficial Intelligence
AI-Powered Devices: The Fourth Wave of Enterprise Computing
Enterprise computing enters its fourth wave as client devices transform from passive endpoints to intelligent AI systems. Modern laptops, desktops, and mobile devices now feature built-in NPUs and AI accelerators that bring artificial intelligence directly to where work happens. This shift creates distributed intelligence networks where every device anticipates user needs, optimizes workflows, and collaborates autonomously. Organizations gain enterprise AI at scale, transforming employees into human AI agents with unprecedented access to contextual, personalized computing experiences. The future of work is here—powered by intelligent devices that don't just access AI, but embody it.
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Overcome roadblocks to GenAI adoption and unlock ROI
GenAI deployments can fall prey to unrealistic goals, misguided pilots, job loss fears, hidden costs and lack of trust. Governance and workforce readiness are keys to success.
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Open source AI: What it means for enterprise innovation
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AI ethical red flags businesses must avoid
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LLM build vs. buy: A decision framework for LLM adoption
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Problem Solve
Businesses face complex cost-cutting options with GenAI
Some GenAI cost saving candidates might disappoint. The best approach to cost reductions vary according to the business function and an enterprise's strategic business priorities.
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GenAI's role in a return trek to the moon and beyond
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Smarter robots: Agentic and physical AI converge in business
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Is GenAI villain and hero in data center power drama?
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Managing drift in AI models and data
The training data and algorithms used to build AI models have a shelf life. Detecting and correcting model drift ensures that these systems stay accurate, relevant and useful.
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7 best practices to avoid AI vendor lock-in
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AI risk management: A strategic guide for enterprise leaders
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How to preprocess different types of data for AI workloads
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Enterprise Artificial Intelligence Basics
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Get Started
Overcome roadblocks to GenAI adoption and unlock ROI
GenAI deployments can fall prey to unrealistic goals, misguided pilots, job loss fears, hidden costs and lack of trust. Governance and workforce readiness are keys to success.
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Get Started
FinOps can manage AI computing costs, experts say
FinOps is no longer just about optimizing cloud spending. Hear from experts on what makes FinOps a desirable option to help businesses manage their AI costs.
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Get Started
History of generative AI innovations spans 9 decades
Rapid GenAI advances are reshaping industries, sparking legal battles and driving embodied AI innovations, paving the way for transformative business processes and tools.
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AI business strategies
U.S. federal AI framework deemed aspirational, noncommittal
The latest executive order is a step toward federal AI regulation. But it's largely noncommittal and shifts most responsibility to Congress, creating an interesting midterm dynamic.
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AI technologies
How simulations and digital twins are advancing robotics
Nvidia GTC 2026 showed the potential of robotics across industries. But these systems must undergo stress testing, and digital twins and simulation are the key.
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AI technologies
Can tokenization free up more data for AI model training?
Research from Capital One Software and PwC suggests enterprises can tap sensitive data to train AI models while balancing predictive power and privacy.
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