Today, we’re thrilled to sit down with Dominic Jainy, a seasoned IT professional whose expertise spans artificial intelligence, machine learning, and blockchain. With a passion for applying cutting-edge technologies across industries, Dominic has a unique perspective on how AI is transforming network engineering, particularly through the use of virtual network labs. In this interview, we’ll explore the evolving role of virtual labs in the AI era, the challenges and opportunities they present, and practical strategies for building and optimizing these environments for future-ready networking. From hardware upgrades to testing innovations, Dominic shares invaluable insights for network professionals looking to stay ahead in a rapidly changing landscape.
How do you see virtual network labs shaping the future of network engineering, especially with AI in the mix?
Virtual network labs are becoming indispensable for network engineers, especially as AI reshapes the field. They provide a safe, cost-effective space to test new configurations and technologies without risking live environments. With AI’s integration into networks, these labs allow engineers to simulate complex scenarios and understand how AI-driven systems interact with traditional infrastructure. They’re not just training grounds; they’re innovation hubs where we can experiment with automation, traffic patterns, and performance monitoring. As AI continues to evolve, labs will be the key to staying ahead, helping engineers adapt to rapid changes without needing expensive physical setups.
What impact has AI had on the way network engineers approach virtual labs?
AI has fundamentally changed the game for virtual labs. It’s no longer just about testing basic connectivity or latency; now, engineers are using labs to explore AI-driven automation, predictive analytics, and even troubleshooting. AI introduces massive computational demands and dynamic traffic patterns that traditional labs weren’t built for, so engineers are rethinking lab design to handle these workloads. It’s pushing us to integrate more powerful hardware and sophisticated testing methods. Honestly, AI is forcing us to upskill fast—labs are where we learn to manage these intelligent, adaptive networks before deploying them in the real world.
What are some of the standout capabilities of virtual labs today when it comes to supporting network tasks?
Right now, virtual labs are pretty solid for a range of tasks. They excel at network automation, letting engineers script configurations and set up rule-based policies with ease. Traffic simulation is another strong area, though it’s often limited to smaller-scale synthetic data. Then there’s performance monitoring—labs can track metrics like latency and throughput quite effectively under standard conditions. These capabilities are great for foundational testing and skill-building, especially for engineers looking to understand how networks behave under controlled scenarios. They’re a fantastic starting point, even if they’re not fully equipped for AI’s heavier demands yet.
What do you see as the biggest hurdles for virtual labs in keeping up with AI-driven networking needs?
The biggest hurdles come down to technical limitations. Most virtual labs struggle to emulate the complex, dynamic traffic patterns that AI applications generate, which makes it hard to test real-world scenarios. They also lack the computational power and storage needed for handling massive datasets or training AI models in real time. GPU acceleration, which is crucial for machine learning tasks, isn’t typically built into these environments. On top of that, many labs don’t have the flexibility to adapt topologies on the fly for AI-optimized networks. These gaps mean we’re often playing catch-up with AI’s pace of innovation.
How can virtual lab architecture be adapted to better support AI workloads?
Adapting lab architecture for AI starts with hardware upgrades. Integrating virtual GPUs is a must to allocate computational power where it’s needed, and high-speed networking—like 100 Gbps Ethernet—helps avoid data bottlenecks. Beyond hardware, dynamic topologies are critical; they let us create and tweak network setups on demand to test AI scenarios. Containerization, using tools like Docker or Kubernetes, is also super helpful for isolating AI workloads so they don’t interfere with other lab functions. Finally, connecting to public cloud services like AWS or Azure can provide access to scalable resources and specialized AI tools that are too expensive to host locally. It’s all about building flexibility and power into the lab’s core design.
In what ways does testing in a virtual lab need to evolve to accommodate AI integration?
Testing has to shift dramatically when AI is involved because these networks are adaptive and constantly learning. Traditional one-off tests won’t cut it anymore; we need continuous integration and delivery pipelines to automate and repeatedly validate new models and setups. Data quality is another huge focus—since AI models are only as good as their training data, testing now includes rigorous checks for data integrity. We also have to go beyond network performance and design specific tests for the AI models themselves, looking at accuracy, bias, and stability under varying conditions. It’s a much broader, ongoing process compared to traditional network testing.
What’s your go-to starting point when building a virtual lab tailored for AI and networking?
The very first step is defining clear use cases. You’ve got to know what AI applications you’re targeting—whether it’s correlation analysis, root cause identification, or augmented troubleshooting. This focus helps shape everything else, from the tools you pick to the hardware you provision. Without a clear goal, you risk building a lab that’s too generic and doesn’t meet specific needs. Once that’s set, I’d move to selecting simulation platforms like Cisco Modeling Labs or EVE-NG to create the virtual network environment. From there, it’s about layering in AI frameworks and ensuring the setup aligns with your objectives. Starting with purpose keeps the project grounded.
What’s your forecast for the future of virtual network labs as AI continues to advance?
I’m optimistic about where virtual network labs are headed with AI. I think we’ll see them become far more powerful and accessible, with built-in support for GPU acceleration and massive data processing as standard features. Integration with cloud platforms will likely deepen, making it easier to scale resources on demand. I also expect testing processes to get smarter—think automated, AI-driven testing that predicts issues before they happen. For network engineers, these labs will become even more critical as AI takes on bigger roles in network management. My forecast is that within a few years, a virtual lab won’t just be a training tool; it’ll be the heart of innovation for building intelligent, self-optimizing networks.