Nex-G Innovations

The Certified Data Center AI Professional (CDCAIP™) program is designed to provide participants with comprehensive knowledge and practical expertise in AI-powered data center technologies, intelligent infrastructure management, GPU and accelerated computing platforms, AI networking, automation, observability, AIOps, cybersecurity, sustainability, and cloud-edge AI architectures.

The training enables professionals to design, deploy, optimize, secure, and manage next-generation AI-ready data centers using modern automation frameworks, predictive analytics, digital twins, and intelligent operational models for hyperscale, enterprise, telecom, and cloud environments.

Code: NES_SK_12409

Duration: 80 Hrs / 8 Weeks / Customized

Mode: Corporate (Instructor Led Online / Onsite), Individual (Instructor Led Online)

Certification: Certified Data Center AI Professional (CDCAIP™)

+91-8826900551

• Evolution of Modern Data Centers
• Traditional vs AI-Driven Data Centers
• AI Infrastructure Ecosystem
• Hyperscale Data Centers
• Colocation and Enterprise AI Facilities
• Edge AI Data Centers
• AI Workload Characteristics
• Data Center Transformation Trends
• AI for IT Operations (AIOps)
• Autonomous Data Centers
• Future of AI Infrastructure

Module 2: Data Center Architecture Fundamentals

• Data Center Tiers and Standards
• Data Center Design Principles
• Compute, Storage, and Networking Overview
• Spine-Leaf Architecture
• High Availability and Redundancy
• Modular Data Centers
• AI Cluster Architecture
• Rack Design for AI Workloads
• GPU Density Planning
• AI Infrastructure Capacity Models

Module 3: AI Compute Infrastructure

• GPU Architecture Fundamentals
• AI Accelerators: GPU, TPU, NPU, FPGA, DPU
• AI Server Design
• High-Density Compute Systems
• AI Training vs Inference Infrastructure
• Multi-GPU Scaling
• GPU Virtualization
• Resource Pooling
• AI Hardware Selection Criteria
• PCIe, NVLink, InfiniBand Concepts
• AI Hardware Lifecycle Management

Module 4: AI Networking for Data Centers

• AI Fabric Requirements
• Ethernet vs InfiniBand
• RDMA and RoCE
• Ultra-Low Latency Networking
• Spine-Leaf for AI Clusters
• Network Congestion Management
• AI Traffic Engineering
• SmartNICs and DPUs
• Time Synchronization
• AI Network Telemetry
• Intent-Based Networking
• Software Defined Networking (SDN)

Module 5: AI Storage Infrastructure

• AI Data Pipelines
• High-Performance Storage Systems
• NVMe and NVMe-oF
• Parallel File Systems
• Object Storage for AI
• Data Lake Architectures
• Storage Tiering
• AI Data Lifecycle Management
• Backup and Disaster Recovery
• AI Dataset Management
• Data Governance

Module 6: AI-Driven Data Center Operations (AIOps)

• Introduction to AIOps
• AI-Based Monitoring
• Intelligent Alert Correlation
• Root Cause Analysis
• Predictive Maintenance
• AI for Incident Management
• Event Analytics
• Log Analytics
• Intelligent Capacity Planning
• Autonomous Remediation
• Self-Healing Infrastructure
• AI-Based SLA Management

Module 7: Data Center Automation & Orchestration

• Infrastructure as Code (IaC)
• Automation Frameworks
• Ansible, Terraform Concepts
• Kubernetes for AI Infrastructure
• Containerization Technologies
• AI Workload Scheduling
• Intelligent Resource Allocation
• Cluster Orchestration
• Policy-Based Automation
• GitOps Principles
• Zero-Touch Provisioning
• Workflow Automation

Module 8: Cooling, Power & Sustainability

• AI Workload Power Challenges
• Power Usage Effectiveness (PUE)
• Thermal Management
• Liquid Cooling Technologies
• Immersion Cooling
• Intelligent Cooling Optimization
• AI-Based Energy Analytics
• Renewable Energy Integration
• Sustainable Data Center Design
• Carbon Footprint Monitoring
• Green AI Infrastructure
• ESG Considerations

Module 9: Data Center Security for AI Infrastructure

• Physical Security
• Cybersecurity Fundamentals
• Zero Trust Architecture
• AI Infrastructure Threat Landscape
• Ransomware Protection
• AI Model Security
• Data Privacy and Compliance
• AI Governance
• Identity & Access Management
• Security Monitoring and SIEM
• AI-Based Threat Detection
• Regulatory Compliance

Module 10: Cloud, Edge & Hybrid AI Data Centers

• Public Cloud AI Infrastructure
• Private AI Clouds
• Hybrid Cloud Models
• Multi-Cloud AI Deployment
• Edge AI Infrastructure
• Distributed AI Architectures
• 5G Edge Data Centers
• AI Workload Placement
• Federated Learning Infrastructure
• Cloud Bursting Concepts
• AI Service Delivery Models

Module 11: Observability, Telemetry & Digital Twins

• Infrastructure Observability
• Metrics, Logs, and Traces
• AI Telemetry Collection
• Real-Time Analytics
• AI-Based Visualization
• Digital Twin Concepts
• Simulation Models
• Failure Prediction Models
• Intelligent Dashboards
• Data Center Command Centers
• Autonomous Operations

Module 12: AI Data Center Design Workshop

• AI Data Center Planning
• GPU Cluster Design Exercise
• AI Rack Density Calculation
• Power & Cooling Planning
• Network Design Workshop
• Storage Sizing Exercise
• Security Architecture Design
• High Availability Design
• AI Capacity Planning
• Cost Optimization Models
• Sustainability Assessment
• End-to-End Architecture Review

Module 13: Industry Standards & Ecosystem

• Data Center Standards Overview
• AI Infrastructure Standards
• Open Compute Project (OCP)
• Uptime Institute Concepts
• Green Grid Initiatives
• Sustainability Standards
• AI Governance Frameworks
• Hyperscaler Best Practices
• Telecom AI Data Centers
• Enterprise AI Infrastructure Trends

Module 14: Case Studies & Real-World Deployments

• Hyperscale AI Data Centers
• AI in Cloud Providers
• Telecom AI Infrastructure
• Financial Sector AI Data Centers
• Healthcare AI Infrastructure
• Smart Manufacturing AI Deployments
• Autonomous Operations Use Cases
• AI Energy Optimization Examples
• GPU Cluster Deployment Lessons
• AI Failure Analysis Scenarios

Module 15: Practical Concepts

• GPU Infrastructure Planning
• Kubernetes AI Cluster Deployment
• AI Monitoring Dashboard
• Predictive Maintenance Model
• AI-Based Cooling Optimization
• Data Center Telemetry Analytics
• Infrastructure Automation
• Observability & AIOps
• AI Capacity Forecasting
• Digital Twin