Cisco Launches New CCDE-AI Infrastructure Certification
The CCDE AI-Infrastructure cert is the first of its kind. It's a vendor-neutral expert-level design certification from the industry's largest provider of enterprise networking technology.
Quick summary: The CCDE-AI Infrastructure is Cisco's newest expert-level design certification, a vendor-neutral validation of advanced skills related to designing AI-optimized network architectures, coming in February 2025.
Artificial intelligence and machine learning have captured the world's attention like no other technology has in a very long time. As news outlets breathlessly cover AI-generated videos and political disinformation, Congress holds hearings about what regulations should apply to it, and students of all ages log into ChatGPT to write their term papers, it is clear that AI is here to stay.
However, the networks that run AI workloads are fundamentally different from traditional business networks. AI-optimized networks must support high-performance computing, massive data throughput, and a radically different power management schema. Differences like these make designing networks capable of supporting AI workloads distinctly and uniquely challenging. Understanding the trade-offs behind each decision is as important as understanding the network's protocols, tools, hardware, and software.
Cisco has announced a vendor-agnostic, expert-level design certification specifically for AI-optimized network architectures. The CCDE-AI Infrastructure certification from Cisco is still in development, but let's unpack what we know about it and what you can do to begin getting ready to earn it.
What is the CCDE-AI Infrastructure Certification?
In early June 2024, the networking world gathered in Las Vegas for the annual Cisco Live conference. There, before a massive crowd of tech enthusiasts, Cisco announced their brand-new CCDE-AI Infrastructure certification. CCDE-AI Infrastructure is a vendor-agnostic, expert-level certification validating skills in designing AI-optimized network architectures. The CCDE-AI Infrastructure will be available in February 2025.
The CCDE-AI Infrastructure will have four main domains:
AI, Machine Learning, Compliance and Governance: This will cover different use cases for AI and how a network can be designed to accommodate them. It will also look at regulations related to data sovereignty and data locality, as well as energy use and cost optimization concerns.
Network: Mostly focusing on the properties and functions that an AI-optimized network provides, this section will also touch on connectivity models and ensuring sufficient bandwidth.
Security: The especially complex nature of AI networks means security has to be built into the infrastructure from the design stage, not implemented later on. This section covers the skills, techniques and tools that can keep AI networks safe.
Hardware and Environment: This section will cover the different hardware configurations that can run AI, how to differentiate between them, and how to choose the best one for different business needs. This section will also cover storage options, log analysis, and correlation tools.
In the Cisco Live announcement about Cisco's new certification and in interviews afterward, Par Merat, Vice President of Cisco Learning and Certifications made it clear that the beating heart of the new certification is the trade-offs that come with implementing AI.
AI-optimized networks can be faster, more powerful, more reliable, and more dynamic, but each implementation decision brings trade-offs. Cisco built the new cert with the understanding that the best AI infrastructure designers will understand and anticipate those trade-offs.
Sustainability and power management are key considerations for businesses investing in AI networks, but there are also compliance issues related to that. Staying compliant with data laws, GDPR and security guidance might cost the network in other areas.
"There's a tradeoff," Par explained in an interview with other members of the Learning & Certifications team, "of what you need from cost-optimization and what you need from data – not just security, but also frequency of updates, and other use-case decisions." Power consumption and trade-offs for costs and everything else related to it lie at the heart of the design certification.
By training network design experts in the trade-offs of different AI technologies, Cisco hopes to empower companies and businesses to make the AI network that is perfect for their operations.
AI is Changing How Networks are Managed and Optimized
Capable of analyzing vast amounts of data in real-time, AI can adaptively and intelligently optimize network performance by predicting network patterns, identifying potential threats, and automating maintenance. AI can also support decision-making by allowing networks to self-configure and self-heal.
Network design is also being rapidly changed by AI. Much more sophisticated and efficient network architectures are possible when AI aids in the design process. Unrestrained by the need for static configurations or manual oversight, AI-driven network design leverages machine learning and predictive analytics. Dynamic and self-optimizing networks can respond in real-time to changes in demand.
In the very near future, companies and networks of all sizes will need network design professionals capable of implementing AI capabilities and understanding the balances and trade-offs that each implementation entails.
CCDE-AI Infrastructure Certification Details
The details of the new Cisco cert haven't been nailed down yet by Cisco, and they're subject to change until the exam is released in February 2025. At the time of this writing, the best resource for the exam topics, eligibility, and format is the existing CCDE certification.
Technically, the CCDE has no formal prerequisites, but attempting the two-hour written exam and 8-hour scenario-based practical exam without extensive familiarity with Cisco technologies and networking skills or substantial Cisco training would be a mistake.
The exam topics of the CCDE-AI Infrastructure will be reflected on the CCDE (400-007) exam v3.1, which is available on February 9, 2025. The exam topics for the CCDE v3.1 include:
Business Strategy Design
Control, Data, Management Plane, and Operational Design
Network Design
Service Design
Security Design
When you begin your CCDE Practical exam, you'll have the option to select the AI-Infrastructure area of expertise, which affects the fourth and final module of the exam. Otherwise, the first three modules will always be the Core (Enterprise tech and topologies) exam.
There are Cisco knowledge bases and technology lists that will apply to the AI-Infrastructure exam, including:
It hasn't been confirmed yet, but it's likely the CCDE AI-Infrastructure will cost the same as the existing CCDE, which is $400 for the written exam and $1,600 for the practical exam.
Why Earn the CCDE-AI Infrastructure Certification?
The CCDE AI-Infrastructure cert is the first of its kind. It's a vendor-neutral expert-level design certification from the industry's largest provider of enterprise networking technology. There are many potential benefits for those who decide to earn it.
For starters, it'll prove and validate your skills related to designing cutting-edge AI-optimized networks. That alone is a huge career benefit, as the demand for AI professionals vastly overwhelms the supply of trained, certified design experts. It's also a brand-new, first-of-its-kind cert, which means earning it proves that you can design and optimize network infrastructures for AI workloads before most people even know what that means.
There aren't many opportunities in a career to get out ahead of the pack in a meaningful and obvious way, but early adopters of the CCDE-AI Infrastructure cert will have the chance to prove they know how to design robust, scalable, and efficient networks that can handle the intensive requirements of AI and machine learning workloads.
The business value of being able to say definitively that you can integrate AI technologies into network designs and develop architectures that are not only optimized for high performance but also capable of supporting dynamic and data-intensive AI operations should be obvious.
According to Cisco research, 90% of companies report having AI aspirations that they are unable to meet. AI isn't going to become less relevant or less pressing any time soon, which means that 90% is not only going to grow, but the depth of their demands and expectations will grow as well. The people positioned to take advantage of that demand will have long, gratifying careers.
How to Prepare for the CCDE AI Infrastructure Certification
There's still a lot about the CCDE-AI Infrastructure that's unknown. Training and preparing for it will be a difficult mix of self-study and extrapolation from existing learning resources. The place to start is with Cisco training. Whatever your level of familiarity with Cisco technologies or networking generally, finding a course that challenges your familiarity with network design or enterprise network concerns will get you one large step closer to the CCDE-AI Infrastructure.
On top of learning about enterprise network design, you'll want to start familiarizing yourself with the ways that AI is currently being implemented, such as with language models like OpenAI's ChatGPT. There are also ethical considerations related to machine learning and AI that you'll want to be familiar with.
Keep an eye on Cisco's training pages and the CBT Nuggets blog, where we'll be posting courses and information about attaining the CCDE-AI Infrastructure as we learn it.
Wrapping Up
The Cisco expert design certification CCDE-AI Infrastructure is an exciting new development in a very fast-moving landscape. AI-powered networks are faster, more efficient, and more capable than anything before them. But they aren't just complex and challenging; they also come with inherent trade-offs and risks that a company needs to be aware of before investing tons of time and effort into developing theirs.
Earning the CCDE-AI Infrastructure proves two key things: first, you've got the technical know-how to design and implement a network that runs AI workloads at optimal conditions. Second, you can think through the operational and business requirements of the company to make smart decisions and recommendations about fundamental, inherent trade-offs of AI-powered networks to deliver the best possible results.
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