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AI Soultions on Cisco Infrastructure Essentials (DCAIE)

The AI Solutions on Cisco Infrastructure Essentials (DCAIE) course covers the essentials of deploying, migrating, and operating AI solutions on Cisco data center infrastructure. You'll be
Referentienummer: GK_UKDCAIE
Leverancier: Global Knowledge
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€3195,00 excl. BTW
The AI Solutions on Cisco Infrastructure Essentials (DCAIE) course covers the essentials of deploying, migrating, and operating AI solutions on Cisco data center infrastructure. You'll be introduced to key AI workloads and elements, as well as foundational architecture, design, and security practices critical to successful delivery and maintenance of AI solutions on Cisco infrastructure.This course is worth 34 Continuing Education (CE) credits toward recertification. Fundamentals of AIIntroduction to Artificial IntelligenceTraditional AITraditional AI Process FlowTraditional AI ChallengesModern Applications of Traditional AIMachine Learning vs. Deep LearningML vs. DL Techniques and MethodologiesML vs. DL Applications and Use CasesGenerative AIGenerative AIGenerative Adversarial FrameworksGenAI Use CasesGenerative AI Inference ChallengesGenAI Challenges and LimitationsGenAI Bias and FairnessGenAI Resource OptimizationGenerative AI vs. Traditional AIGenerative AI vs. Traditional AI Data RequirementsFuture Trends in AIAI Language ModelsLLMs vs. SLMsAI Use CasesAnalyticsNetwork OptimizationNetwork Automation and Self-Healing
The AI Solutions on Cisco Infrastructure Essentials (DCAIE) course covers the essentials of deploying, migrating, and operating AI solutions on Cisco data center infrastructure. You'll be introduced to key AI workloads and elements, as well as foundational architecture, design, and security practices critical to successful delivery and maintenance of AI solutions on Cisco infrastructure.This course is worth 34 Continuing Education (CE) credits toward recertification. Fundamentals of AIIntroduction to Artificial IntelligenceTraditional AITraditional AI Process FlowTraditional AI ChallengesModern Applications of Traditional AIMachine Learning vs. Deep LearningML vs. DL Techniques and MethodologiesML vs. DL Applications and Use CasesGenerative AIGenerative AIGenerative Adversarial FrameworksGenAI Use CasesGenerative AI Inference ChallengesGenAI Challenges and LimitationsGenAI Bias and FairnessGenAI Resource OptimizationGenerative AI vs. Traditional AIGenerative AI vs. Traditional AI Data RequirementsFuture Trends in AIAI Language ModelsLLMs vs. SLMsAI Use CasesAnalyticsNetwork OptimizationNetwork Automation and Self-Healing