Let’s make it work for you
Overview
This course provides a comprehensive introduction to designing, implementing, operating, and troubleshooting AI-enabled data centre infrastructure using Cisco technologies. Learners will explore how artificial intelligence and machine learning workloads influence modern data centre design, networking, compute, and storage decisions.
The course focuses on the practical requirements of supporting AI workloads at scale, including high-performance networking, workload placement, security, sustainability, and day-two operations. Learners will examine how AI infrastructure is monitored and optimised using telemetry, analytics, and automation tools, alongside hands-on exposure to troubleshooting techniques for complex AI and machine learning environments.
Prerequisites
There are no formal prerequisites for this course. However, learners are expected to have prior knowledge of core data centre technologies and networking concepts.
Recommended knowledge includes an understanding of Cisco Unified Computing System architecture and operations, familiarity with Cisco Nexus switching technologies, and experience with data centre core networking principles. Learners who have previously studied Cisco data centre fundamentals or have hands-on operational experience will benefit most from this course.
Target audience
This course is designed for technical professionals responsible for designing, implementing, or supporting data centre infrastructure that enables AI and machine learning workloads.
Typical roles include network engineers, data centre engineers, systems engineers, storage administrators, technical architects, and consulting engineers. It is also suitable for technical leads and programme or project managers who require a deeper understanding of AI infrastructure requirements and operational considerations within modern data centres.
Delegates will learn how to
By the end of this course, learners will be able to:
- Describe key artificial intelligence concepts, including traditional AI, machine learning, deep learning, and generative AI, and explain their impact on data centre infrastructure
- Explain how AI technologies enhance network operations through automation, predictive analytics, and anomaly detection
- Describe the architecture and operational principles of AI and machine learning clusters, including model acquisition, optimisation, and lifecycle management
- Identify the essential components required to build robust, scalable, and sustainable AI infrastructure
- Design and evaluate workload placement strategies to ensure performance, interoperability, and cost efficiency
- Explain the network requirements for AI workloads, including transport technologies, congestion management, and lossless fabric design
- Monitor, operate, and troubleshoot AI infrastructure using telemetry, analytics, and modern observability tools
Outline
Foundations of artificial intelligence
- Introduction to artificial intelligence concepts and terminology
- Traditional AI, machine learning, and deep learning techniques
- Overview of generative AI, its challenges, and emerging trends
- Common AI and machine learning use cases in data centre environments
AI clusters, models, and tooling
- Architecture and core components of AI and machine learning clusters
- Introduction to pre-trained models and fine-tuning approaches
- Using Jupyter-based environments to support AI-driven automation
- Applying generative AI tools to enhance operational productivity
AI infrastructure fundamentals
- Key infrastructure components required to support AI workloads
- Compute, networking, and storage considerations for AI environments
- Workload placement strategies and interoperability requirements
- AI-specific policies, governance frameworks, and compliance considerations
- Designing sustainable AI infrastructure with a focus on efficiency and cost optimisation
Networking for AI workloads
- Key network challenges introduced by AI and machine learning applications
- Transport technologies supporting AI workloads, including optical and copper connectivity
- Network connectivity models and architectural design patterns
- Layer 2 and Layer 3 protocols supporting distributed AI processing
- Migration strategies from traditional data centre networks to AI-optimised fabrics
High-performance Ethernet and lossless fabrics
- Overview of RDMA and RoCE mechanisms and operations
- Designing high-throughput, low-latency Ethernet fabrics
- Quality of service tools for lossless network design
- Understanding congestion management using ECN and PFC
- Monitoring congestion and performance using Cisco Nexus Dashboard Insights
Data preparation and performance considerations
- Introduction to data preparation processes for AI and machine learning
- Understanding how data performance impacts AI workload efficiency
- Monitoring AI and machine learning traffic flows
- Evaluating infrastructure behaviour across different AI workload stages
AI-enabling hardware and virtualisation
- AI-specific compute hardware and accelerators
- Compute resource architectures for AI workloads
- Virtual infrastructure options and deployment considerations
- Storage protocols, software-defined storage, and data access strategies
AI infrastructure deployment and operations
- Setting up and configuring AI clusters
- Using fabric management tools to optimise AI workloads
- Deploying and using locally hosted generative AI models with retrieval-augmented generation
- Operational monitoring of AI infrastructure components
Troubleshooting AI infrastructure
- Common issues in AI and machine learning fabrics
- Troubleshooting performance, connectivity, and congestion problems
- Using telemetry and analytics to diagnose infrastructure issues
- Applying structured troubleshooting methodologies in AI-enabled data centres
Exams and assessments
This course prepares learners for the Implementing Cisco Data Center AI Infrastructure 300-640 DCAI exam. The exam assesses knowledge of AI infrastructure design, implementation, monitoring, and troubleshooting within data centre environments.
There are no formal assessments during the course itself. Knowledge checks and practical exercises are used throughout the training to reinforce learning and prepare learners for the certification exam.
Hands-on learning
This course includes practical exercises designed to reinforce key concepts and support real-world application. Learners will work with AI infrastructure tooling to explore monitoring, configuration, and troubleshooting scenarios. Hands-on activities focus on applying theoretical knowledge to realistic data centre environments and operational challenges.
Frequently asked questions
How can I create an account on myQA.com?
There are a number of ways to create an account. If you are a self-funder, simply select the "Create account" option on the login page.
If you have been booked onto a course by your company, you will receive a confirmation email. From this email, select "Sign into myQA" and you will be taken to the "Create account" page. Complete all of the details and select "Create account".
If you have the booking number you can also go here and select the "I have a booking number" option. Enter the booking reference and your surname. If the details match, you will be taken to the "Create account" page from where you can enter your details and confirm your account.
Find more answers to frequently asked questions in our FAQs: Bookings & Cancellations page.
How do QA’s virtual classroom courses work?
Our virtual classroom courses allow you to access award-winning classroom training, without leaving your home or office. Our learning professionals are specially trained on how to interact with remote attendees and our remote labs ensure all participants can take part in hands-on exercises wherever they are.
We use the WebEx video conferencing platform by Cisco. Before you book, check that you meet the WebEx system requirements and run a test meeting to ensure the software is compatible with your firewall settings. If it doesn’t work, try adjusting your settings or contact your IT department about permitting the website.
How do QA’s online courses work?
QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course.
Once you have purchased the Online course and have completed your registration, you will receive the necessary details to enable you to immediately access it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.
All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.
When will I receive my joining instructions?
Joining instructions for QA courses are sent two weeks prior to the course start date, or immediately if the booking is confirmed within this timeframe. For course bookings made via QA but delivered by a third-party supplier, joining instructions are sent to attendees prior to the training course, but timescales vary depending on each supplier’s terms. Read more FAQs.
When will I receive my certificate?
Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.