This course provides an in-depth understanding of designing Cisco networks tailored for artificial intelligence (AI) applications and solutions. Participants will learn how to leverage Cisco technologies to design, build and operate next-generation network infrastructure to support AI workloads effectively. The course covers key concepts, best practices, and practical implementation strategies for AI-ready networks using Cisco solutions.
Prerequisites:
The knowledge and skills that the learner should have before attending this course are as follows:
- CCNA or equivalent experience
- Basic Knowledge of AI Systems
- Storage Admin Familiarity
Course Objectives:
Upon completing this course, the learner will be able to meet these overall objectives:
- Understand Cisco Networking Technologies for AI
- Describe AI and Network Design
- Understand AI Workloads and Network Requirements
- Learn Security and Compliance for AI Networks
- Understanding the components if AI Infrastructure
Module 1: Introduction to AI and Network Design
- Understanding the emergence of AI and its practical applications
- Overview of Cisco technologies for AI integration
- Challenges and considerations in designing AI-ready networks
Module 2: Understanding the components if AI Infrastructure
- High Performance GPU Cloud
- High Performance Storage and Storage Networks
- High Performance Ethernet Networks
- High Performance Compute
- Data Processing and Machine Learning Frameworks
- MLOps Platforms
Module 3: Understanding AI Workloads and Network Requirements
- Kubernetes vs. Docker/Container
- Workload characterization and Generative AI Workload
- Distributed Workloads and Computing
- Network architecture requirements for AI applications
- Scalability, performance, and latency considerations
- Integration of high-speed data pipelines for AI model training and inference
Module 4: Cisco Networking Technologies for AI
- Deep dive into Cisco switches, routers, and data center fabrics
- Introduction to SONiC and SONiC on Cisco 8000
- Deep Dive into SONiC for AI Infrastructure Networking in the Hybrid Cloud Environments
- Neural Networks and Parallelism
- Cisco NDFC for network automation and policy-based management on Nexus Spine and Leaf Architecture
- Integration of Cisco UCS for AI workload optimization
Module 5: Security and Compliance for AI Networks
- Threat landscape for AI environments
- Implementing security controls for data privacy and confidentiality
- Compliance considerations for AI data handling and processing
Module 6: Network Optimization Techniques for AI
- Quality of Service (QoS) for prioritizing AI traffic
- Network slicing for efficient resource allocation
- Load balancing and traffic engineering for AI workloads
Module 7: Monitoring and Analytics for AI Networks
- Utilizing Cisco Observability Solutions for AI Networks
- AI-driven analytics for proactive network management
- MLOps Tools
- Troubleshooting and optimization techniques for AI workloads connectivity challenges
Module 8: Case Studies and Best Practices
- Real-world examples of AI network designs using Cisco technologies
- Best practices for deploying and managing AI-ready networks
- Lessons learned and future trends in AI network design
Module 9: Hands-on Labs and Practical Exercises
- Lab exercises covering network design, configuration, and optimization
- Simulation of AI workloads to test network performance
- Troubleshooting scenarios and best practices demonstration
Key Takeaways:
- Resources for further learning and certification pathways
- Recommendations for implementing AI-ready networks in real-world environments
The primary audience for this course is as follows:
- Network Solutions Architects
- Presales Architects
- Systems Engineers
- Channel Partners
- AI Infrastructure Administrators
- AI Engineers
- ML Engineers
- Technical Managers and Decision Makers