Should Network Engineers Learn AI and Machine Learning?
Today, networking systems are growing to be more complex in terms of structure and design. The reason behind this is digital transformation, multiple cloud services, an increased number of devices, data, and hybrid work styles. In fact, they are getting so complex that it has mantled the ability of the network engineers to effectively handle them. Modern networks demand network engineers with the modern skills to manage, optimize, and secure these increasingly complex systems. This is where AI and ML become vital for network engineers. Applications of AI and ML in networking contribute to maintaining smooth and coherent network operation, which in turn means fewer mistakes and better predictions. For businesses, this means less downtime, happier customers, and more efficient use of resources. Now, you might be asking yourself, “Should I, as a network engineer, invest my time and effort in learning these technologies?” The answer is YES. In this guide, we will help you get an idea of why AI and ML are the future and why you should learn these technologies. If you want to learn these leading technologies, you can take the Artificial Intelligence and Machine Learning Course. Before getting into more details, let us first understand what AI and ML really are in terms of network environments. AI refers to the simulation of human intelligence processes by machines, particularly computer systems. This includes various capabilities such as learning, reasoning, and self-correction. Machine learning, a subset of AI, focuses on the development of algorithms that can learn from and make predictions based on data. For AI to enhance networking effectively, it relies heavily on Machine Learning (ML). ML uses algorithms to analyze network data, learn from patterns, and make predictions about network performance without needing direct commands. Recent advancements in computing power and storage have allowed ML to develop into more sophisticated models, such as deep learning (DL), which utilizes neural networks for deeper insights and greater network automation. In short, ML is a subset of AI, which is focused on learning from data and improving through experience. While Deep Learning is a subset of ML, which is used to process way more complex data and then make predictions. Machine learning algorithms can process massive datasets, enabling faster decision-making than humans. This is particularly beneficial for online systems requiring rapid responses to changes. Additionally, tools like natural language processing (NLP), large language models (LLM), and generative AI for network optimization (GenAI) are also playing a crucial role in improving networking capabilities. These technologies assist in creating smarter virtual assistants and optimizing workflows, ultimately making networks more efficient and easier to manage. Integrating AI and ML into networking not only streamlines operations but also helps predict issues before they affect performance. Now, back to your question “Why you should invest your time and effort in learning AI and ML?” Every time people discuss networking, the first thing that comes to their mind is Cisco Systems which has almost 90% of the market share. This leadership position is being further strengthened by Cisco’s massive and strategic drift towards AI and ML. This recent declaration of a $1 billion investment by Cisco in Artificial Intelligence (AI) has opened lots of new career opportunities. AI is transforming all sectors more rapidly than any other invention in human history. According to the IDC (2023) research, the AI market will double its size in the next three years, reaching a $500+ billion market. This fast growth therefore offers a good reason for anyone who is seeking a jump in their professional career. For network engineers, the implications of Cisco’s strategic moves are clear: adoption of AI and ML is no longer a choice. While Cisco is currently at the forefront of incorporating these technologies, network engineers who make the effort to enhance their understanding of AI and ML will set themselves up for future innovations within the IT industry. Now, you have a pretty good knowledge that the coming years belong to AI and ML. Hence, putting effort into learning AI and ML skills will put you in a safe space. Now, let us understand the different use cases of AI and ML in networking. This will give you an idea of how these technologies can help you handle and reduce modern network complexity. In the context of networking, AI and ML can be applied in a variety of ways. We have also shown with the help of an image how AI and ML can assist in making network environments more reliable and secure. One of the primary reasons for network engineers to gain knowledge in AI and ML is the enhanced network management they provide. Many of the current approaches to managing and maintaining a network are done in a traditional manner and involve the use of simple tools and personnel. This can be time-consuming, hence leading to a lot of human error, jeopardizing the results of the business. Integrating AI and ML into networking tools enables you to automate routine tasks, enabling better performance and efficiency. For instance, existing AI algorithms can instantly scan a large amount of network data and determine trends that pose a security risk or network problems. This enables network engineers to protect the network from anomalies rather than waiting for their clients to inform them of the problem. It is also important to note that machine learning models should be able to learn and get better with more data and further assist in predictive maintenance. This means that as network environments evolve, your tools can help you predict future problems and prevent them from happening. Cybersecurity is another critical area where network engineers can use AI and ML. Today’s networks are exposed to numerous threats, including malware, phishing, and many other attacks. This is why the existing security solutions may be inadequate to protect the systems against these ever-morphing threats. When it comes to AI and ML, it enables network engineers to provide intelligent solutions to provide security and measures that use the intelligence algorithms that are integrated to identify, view, and act on security threats. For example, with the help of AI and machine learning models, it is possible to study users’ activity and determine that some actions may indicate a violation. AI for cybersecurity can certainly assist in threat detection and respond to threats in a more efficient manner than humans, which will consequently aid in the fast-tracking and mitigation processes. You can thereby develop a more secure network environment using these technologies and save your organization’s important data and reputation. The IoT devices have exploded and there is an increase in media usage that makes network traffic congested. AI and ML can help network engineers maintain and control traffic, offering better network efficiency. With Machine Learning, it becomes possible for applications to learn more about traffic flow and make adaptations to it to improve bandwidth distribution. This helps maintain optimal performance even during peak usage times. Furthermore, through dynamic routing based on AI, it is possible to optimize paths for data transfer and, therefore, optimize connections and response time to the user. These algorithms work by reviewing past experience and drawing correct understandings of the ideal pathways for data packets to observe, thereby leading to more efficient and reliable connections. One of the many use cases of AI and ML in networking is intelligent documentation. Traditionally, documentation of networks is time-consuming, especially since updates were done manually and needed frequent updates. This can result in the spread of old or wrong information, which may work against the network engineers as they try to solve problems. Using Artificial Intelligence and Machine Learning, it is possible to automate the documentation process. For instance, AI can perform some tasks that require updating the network configurations and network changes, ensuring that documentation is always up-to-date. But this also saves a lot of time and enables engineers to avoid making mistakes, thereby directing their energy and attention to other important issues. Another valuable application of AI and ML in networking is the use of chatbots for support. Those AI-driven helpers or virtual network assistants can work on simple questions and, therefore, offer immediate help to the users while engineers deal with complicated issues. For instance, if the user has questions as to why the device is not connecting as expected or steps on how to connect it, the chatbot can answer the queries. This on-demand help improves user experience and also frees a lot of time for IT departments. Further, they can also gain conversation knowledge as time goes on and become more efficient in their answers as well as more extensive in their data, which will, in turn, enhance the support they afford users. Another important use case of AI and ML is the prediction of users’ experiences in a given network. Self-learning algorithms can analyze user behavior and analyze the performance of the network to find patterns that may help in the early prediction of problems for users. For instance, if the system indicates that bandwidth has reduced during peak usage, then the network engineers have prior information on how to go about the network and save on the costs, such as by adjusting traffic flow or resource allocation. This predictive ability, for instance, enables users to have a perfect run through the systems without many hitches that may cause dissatisfaction. As user needs change and evolve, machine learning models continue to adapt, providing increasingly accurate predictions that help maintain optimal network performance. It is evident that IT is unable to provide business support and meet today’s complex network demands without an AI and ML solution. Below are several technological components that an AI and ML solution should include: AI and ML are used to provide information that will help networking solutions be adopted at the right time. They can scan large amounts of network data in time, distinguishing useful patterns and tendencies to make decisions. Advanced algorithms can help to analyze structured and unstructured data, including traffic density and users’ actions, and pinpoint the information that network engineers may find useful. This implies that organizations can undertake informed decisions that enhance the efficiency as well as performance of the network as a whole. The use of an efficient pattern of AI and ML can enable the provision of the right response according to any situation encountered. These technologies can assist in automating responses to most of the problems that may occur within a network. For instance, in a case where there is an alarm indication of a threat on the networks, AI can cause a response mechanism without asking for permission. This fast response not only reduces the time duration but also protects the network. Moreover, AI systems can adapt to the interactions and refine their action on subsequent incidences, thus enhancing the efficacy of the network administration. AI and ML are also used in developing the right infrastructure for networking solutions. From the evaluations of present-day network performance and utilization of resources, these technologies give the organization the ability to design networks that respond to changing requirements. Advanced analysis by the AI engine means that the bandwidth resources are properly managed so that congestion is rare when many people are accessing a site. This leads to better network resilience and better network scalability and offers the best network infrastructure that is able to adapt to user demand or application needs. Artificial Intelligence in networking won’t affect network engineer jobs as it will assist network engineer roles by automating tasks, enhancing efficiency, and enabling a focus on strategic decision-making. There are many courses that will enhance the skills and are best suited for network engineers. Some of these are: Yes, Machine Learning is used in networking. Some examples associated with machine learning usage include routing table updates, traffic analysis, anomaly detection, and many more. Yes, computer networking is related to AI. AI enhances network management, automates network troubleshooting, improves security, and optimizes traffic flow through intelligent data analysis and decision-making. The combination of networking with artificial intelligence and machine learning is not a trend; it will become a necessity for a successful career. For a network engineer, knowledge of these technologies will enhance skills, heighten security measures, streamline networks, and improve the optimization of networks as well as their management. Al and ML as a new concept may be intimidating to some, but as a network engineer, dedicating time and effort to understand these new concepts will not only boost your career but equip you with new tools that shape modern networking. By adopting these technologies, you can provide better solutions for the complicated problems related to network management, optimization, and network security, creating and contributing to a more efficient, robust, and secure environment. The future of networking is intelligent, and you should be too.Introduction
What are Artificial Intelligence and Machine Learning in Networking?
Why Network Engineers Should Learn AI and ML?
Use Cases of Artificial Intelligence and Machine Learning in Networking
Enhancing Network Management
Improved Network Security
Traffic Management and Optimization
Intelligent Documentation
Chatbots for Support
Predicting User Experiences
What do AI and ML Offer in Terms of Networking Solutions?
The Right Data
The Right Response
The Right Infrastructure
Frequently Asked Questions
Q1. Will AI affect network engineer jobs?
Q2. Which AI course is best for network engineers?
Q3. Is Machine Learning used in networking?
Q4. Is computer networking related to AI?
Conclusion
