Name: Implementing Cisco Data Center AI Infrastructure (DCAI)
Availability: InStock
Rating: 4.6 (5878 reviews)

Duration:

40.00 hours

32.5 CPD hours

Overview:

The Implementing Cisco Data Center AI Infrastructure (DCAI) training is designed to equip professionals with the skills to support, secure, and optimize AI workloads within modern data center environments. This comprehensive program delves into the unique characteristics of AI/ML applications, their influence on infrastructure design, and best practices for automated provisioning. Participants will gain in-depth knowledge of security considerations for AI deployments and master day-2 operations, including monitoring and advanced troubleshooting techniques such as log correlation and telemetry analysis. Through hands-on experience, including practical application with tools like Splunk, learners will be prepared to efficiently monitor, diagnose, and resolve issues in AI/ML-enabled data centers, ensuring optimal uptime and performance for critical organizational workloads.

This training combines content from Operate and Troubleshoot AI Solutions on Cisco Infrastructure (DCAIAOT) and AI Solutions on Cisco Infrastructure Essentials (DCAIE) training.

This training prepares you for the 300-640 DCAI v1.0 exam. If passed, you earn the Cisco Certified Specialist - Data Center AI Infrastructure certification and satisfy the concentration exam requirement for the Cisco Certified Network Professional (CCNP) Data Center certification.

What to Expect in the Exam

Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) v1.0 is a 90-minute exam associated with the Cisco Certified Specialist - Data Center AI Infrastructure certification and satisfies the concentration exam requirement for the CCNP Data Center certification.

This exam tests your knowledge of AI infrastructure, including:

- Design
- Implementation
- Monitoring
- Troubleshooting

How You'll Benefit

This training will help you:

- Acquire comprehensive skills to support, secure, and optimize AI workloads within modern data center environments
- Understand the design, implementation, and advanced troubleshooting of AI infrastructure, including network challenges and specialized hardware
- Gain in-depth knowledge of AI/ML concepts, generative AI, and their practical application in network management and automation
- Apply hands-on techniques for monitoring, diagnosing, and resolving issues, leveraging tools like Splunk and utilizing AI for enhanced productivity in network operations
- Prepare for the 300-640 DCAI v1.0 exam
- Earn 38 CE credits toward recertification

Description:

The Implementing Cisco Data Center AI Infrastructure (DCAI) training is designed to equip professionals with the skills to support, secure, and optimize AI workloads within modern data center environments. This comprehensive program delves into the unique characteristics of AI/ML applications, their influence on infrastructure design, and best practices for automated provisioning. Participants will gain in-depth knowledge of security considerations for AI deployments and master day-2 operations, including monitoring and advanced troubleshooting techniques such as log correlation and telemetry analysis. Through hands-on experience, including practical application with tools like Splunk, learners will be prepared to efficiently monitor, diagnose, and resolve issues in AI/ML-enabled data centers, ensuring optimal uptime and performance for critical organizational workloads.

This training combines content from Operate and Troubleshoot AI Solutions on Cisco Infrastructure (DCAIAOT) and AI Solutions on Cisco Infrastructure Essentials (DCAIE) training.

This training prepares you for the 300-640 DCAI v1.0 exam. If passed, you earn the Cisco Certified Specialist - Data Center AI Infrastructure certification and satisfy the concentration exam requirement for the Cisco Certified Network Professional (CCNP) Data Center certification.

What to Expect in the Exam

Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) v1.0 is a 90-minute exam associated with the Cisco Certified Specialist - Data Center AI Infrastructure certification and satisfies the concentration exam requirement for the CCNP Data Center certification.

This exam tests your knowledge of AI infrastructure, including:

- Design
- Implementation
- Monitoring
- Troubleshooting

How You'll Benefit

This training will help you:

- Acquire comprehensive skills to support, secure, and optimize AI workloads within modern data center environments
- Understand the design, implementation, and advanced troubleshooting of AI infrastructure, including network challenges and specialized hardware
- Gain in-depth knowledge of AI/ML concepts, generative AI, and their practical application in network management and automation
- Apply hands-on techniques for monitoring, diagnosing, and resolving issues, leveraging tools like Splunk and utilizing AI for enhanced productivity in network operations
- Prepare for the 300-640 DCAI v1.0 exam
- Earn 38 CE credits toward recertification

What You'll Learn:

- Describe key concepts in artificial intelligence, focusing on traditional AI, machine learning, and deep learning techniques and their applications
- Describe generative AI, its challenges, and future trends, while examining the nuances between traditional and modern AI methodologies
- Explain how AI enhances network management and security through intelligent automation, predictive analytics, and anomaly detection
- Describe the key concepts, architecture, and basic management principles of AI-ML clusters, as well as describe the process of acquiring, fine-tuning, optimizing and using pre-trained ML models
- Use the capabilities of Jupyter Lab and Generative AI to automate network operations, write Python code, and leverage AI models for enhanced productivity
- Describe the essential components and considerations for setting up robust AI infrastructure
- Evaluate and implement effective workload placement strategies and ensure interoperability within AI systems
- Explore compliance standards, policies, and governance frameworks relevant to AI systems
- Describe sustainable AI infrastructure practices, focusing on environmental and economic sustainability
- Guide AI infrastructure decisions to optimize efficiency and cost
- Describe key network challenges from the perspective of AI/ML application requirements
- Describe the role of optical and copper technologies in enabling AI/ML data center workloads
- Describe network connectivity models and network designs
- Describe important Layer 2 and Layer 3 protocols for AI and fog computing for Distributed AI processing
- Migrate AI workloads to dedicated AI network
- Explain the mechanisms and operations of RDMA and RoCE protocols
- Understand the architecture and features of high-performance Ethernet fabrics
- Explain the network mechanisms and QoS tools needed for building high-performance, lossless RoCE networks
- Describe ECN and PFC mechanisms, introduce Cisco Nexus Dashboard Insights for congestion monitoring, explore how different stages of AI/ML applications impact data center infrastructure, and vice versa
- Introduce the basic steps, challenges, and techniques regarding the data preparation process
- Use Cisco Nexus Dashboard Insights for monitoring AI/ML traffic flows
- Describe the importance of AI-specific hardware in reducing training times and supporting the advanced processing requirements of AI tasks
- Understand the compute hardware required to run AI/ML solutions
- Understand existing intelligence and AI/ML solutions
- Describe virtual infrastructure options and their considerations when deploying
- Explain data storage strategies, storage protocols, and software-defined storage
- Use NDFC to configure a fabric optimized for AI/ML workloads
- Use locally hosted GPT models with RAG for network engineering tasks

Skills Taught:

- Describe key concepts in artificial intelligence, focusing on traditional AI, machine learning, and deep learning techniques and their applications
- Describe generative AI, its challenges, and future trends, while examining the nuances between traditional and modern AI methodologies
- Explain how AI enhances network management and security through intelligent automation, predictive analytics, and anomaly detection
- Describe the key concepts, architecture, and basic management principles of AI-ML clusters, as well as describe the process of acquiring, fine-tuning, optimizing and using pre-trained ML models
- Use the capabilities of Jupyter Lab and Generative AI to automate network operations, write Python code, and leverage AI models for enhanced productivity
- Describe the essential components and considerations for setting up robust AI infrastructure
- Evaluate and implement effective workload placement strategies and ensure interoperability within AI systems
- Explore compliance standards, policies, and governance frameworks relevant to AI systems
- Describe sustainable AI infrastructure practices, focusing on environmental and economic sustainability
- Guide AI infrastructure decisions to optimize efficiency and cost
- Describe key network challenges from the perspective of AI/ML application requirements
- Describe the role of optical and copper technologies in enabling AI/ML data center workloads
- Describe network connectivity models and network designs
- Describe important Layer 2 and Layer 3 protocols for AI and fog computing for Distributed AI processing
- Migrate AI workloads to dedicated AI network
- Explain the mechanisms and operations of RDMA and RoCE protocols
- Understand the architecture and features of high-performance Ethernet fabrics
- Explain the network mechanisms and QoS tools needed for building high-performance, lossless RoCE networks
- Describe ECN and PFC mechanisms, introduce Cisco Nexus Dashboard Insights for congestion monitoring, explore how different stages of AI/ML applications impact data center infrastructure, and vice versa
- Introduce the basic steps, challenges, and techniques regarding the data preparation process
- Use Cisco Nexus Dashboard Insights for monitoring AI/ML traffic flows
- Describe the importance of AI-specific hardware in reducing training times and supporting the advanced processing requirements of AI tasks
- Understand the compute hardware required to run AI/ML solutions
- Understand existing intelligence and AI/ML solutions
- Describe virtual infrastructure options and their considerations when deploying
- Explain data storage strategies, storage protocols, and software-defined storage
- Use NDFC to configure a fabric optimized for AI/ML workloads
- Use locally hosted GPT models with RAG for network engineering tasks

Available Delivery Options for Implementing Cisco Data Center AI Infrastructure (DCAI)

Live Instructor Led Classroom Online (Live Online)
Traditional Instructor Led Classroom training (TILT/ILT)
Delivery at your offices in United Kingdom
Private dedicated course for your staff

What certification does Implementing Cisco Data Center AI Infrastructure (DCAI) prepare you for?

The Implementing Cisco Data Center AI Infrastructure (DCAI) course helps prepare you for the Cisco Certified Specialist - Data Center AI Infrastructure, Cisco Certified Network Professional (CCNP) Data Center certification path.

Which exam does Implementing Cisco Data Center AI Infrastructure (DCAI) prepare you for?

The Implementing Cisco Data Center AI Infrastructure (DCAI) prepares you for the '300-640' official exam. You can take this exam at any exam center across United Kingdom including London, Manchester, Birmingham, Edinburgh or live online wherever you are.

How many CPD hours does Implementing Cisco Data Center AI Infrastructure (DCAI) provide?

The 5 day Implementing Cisco Data Center AI Infrastructure (DCAI) course gives you up to 32.5 CPD hours/structured learning hours.

Who is the correct audience for Implementing Cisco Data Center AI Infrastructure (DCAI)?

- Network Designers
- Network Administrators
- Storage Administrators
- Network Engineers
- Systems Engineers
- Data Center Engineers
- Consulting Systems Engineers
- Technical Solutions Architects
- Cisco Integrators/Partners
- Field Engineers
- Server Administrators
- Network Managers
- Program Managers
- Project Managers

Do you provide training for Implementing Cisco Data Center AI Infrastructure (DCAI)?

Yes, we provide corporate training, dedicated training and closed classes for Implementing Cisco Data Center AI Infrastructure (DCAI). This can take place anywhere in United Kingdom including London, Manchester, Birmingham, Edinburgh or live online.

What is the duration of Implementing Cisco Data Center AI Infrastructure (DCAI)?

The Implementing Cisco Data Center AI Infrastructure (DCAI) training takes place over 5 day(s), with each day lasting approximately 40.00 hours including breaks.

What other terms do people search for?

Popular related searches include: Implementing Cisco Data Center AI Infrastructure, Cisco AI Solutions, DCAI

Why are Nexus Human the best provider for Implementing Cisco Data Center AI Infrastructure (DCAI)?

Nexus Human are recognised as one of the best training companies. Our trainers have won awards including Small Firms Best Trainer, national training partner of the year for Ireland, and global top 30 instructor awards in 2012, 2019 and 2021.

Is there a discount code for Implementing Cisco Data Center AI Infrastructure (DCAI)?

Yes, the discount code PENPAL5 is currently available for Implementing Cisco Data Center AI Infrastructure (DCAI) training. Only one discount code can be used per booking.

Implementing Cisco Data Center AI Infrastructure (DCAI)