AI/ML-BASED NETWORK MANAGEMENT

[Virtual Presenter] Good morning everyone. Today, I am going to discuss the use of Artificial Intelligence and Machine Learning to effectively manage networks. We will look at how AI and ML can be used to analyze data and provide helpful insights on how to better manage networks. Let's get started!.

[Audio] AI has undergone an evolution in its definition since the first days of its conception. Initially, AI evaluation focused on whether a machine could pass the Turing Test, or make a human believe they were conversing with another person. However, the concept has developed to the point where AI researchers are now utilizing probability and heuristics to program machines. This shift in view allows us to control the potential hype and fears that the media may generate surrounding the field, and achieve realistic and attainable goals..

[Audio] Artificial intelligence has become the optimal choice for network management due to its efficient combination of computing and human-like decision-making. AI is able to quickly sort through massive amounts of data to identify potential issues. Furthermore, AI is incredibly organized and focused when attempting to solve complicated problems, taking into account the concept of probability to provide reliable solutions much faster than a human. With all these benefits, it is clear why AI is becoming the go-to choice for network management..

[Audio] This slide examines the varied types of Artificial Intelligence. Reactive machines are AI systems that possess only short-term memory and can simply react to current signals. Limited memory machines apply past experiences to choose current decisions while theory of mind machines consider the objectives, views and thoughts of other bodies. Self-aware machines are AI systems that are aware of their own function, history and behavior. Supervised learning, a subcategory of machine learning and Artificial Intelligence, uses labeled data sets to assign data or accurately guess results. Supervised learning aids organizations by providing a solution to several practical problems on a large scale..

[Audio] Using unsupervised machine learning algorithms, we can analyze and cluster datasets that are unlabeled, seeking out patterns that are undetectable to the human eye. Let's explore how these insights can be applied to other areas of life..

[Audio] Reinforcement learning is a type of machine learning that allows a system to learn through the use of rewards or punishments for performing certain actions. This powerful tool is widely used in AI/ML-based network management, and this article will share a personal account of someone who has been successful with its implementation..

[Audio] Artificial Intelligence has an interesting future, and this presentation will concentrate on a single use of it - AI/ML-based network management. AI/ML-based network management is a revolutionary technology which can totally change how networks will be managed and maintained. AI/ML-based network management works by using AI and machine learning to automate regular procedures, optimize network performance, and discover potential issues and irregularities within the system. With AI/ML-based network management, network administrators will be able to manage large, complex, and distributed networks more accurately and effectively, guaranteeing that the systems remain secure and dependable..

[Audio] Machine Learning is a highly useful tool that is transforming automation processes. It provides an effective way to identify problems and make evaluations and resolutions quickly. For instance, if the system identifies odd activity on a user account, AI can take a "triage" approach to assign resources promptly and competently to the account. This makes sure that our impact is maximized and that we do not use up resources on unnecessary investigations..

[Audio] AI and ML have had a significant impact on our network management process. They enable us to quickly and efficiently identify and resolve issues. With the help of these technologies, we can address four different aspects: traffic management, performance monitoring, capacity planning and security monitoring. Machine Learning is used to quickly and accurately anticipate and identify issues, helping us to remain ahead of any present or potential issues..

[Audio] Artificial Intelligence (AI), Machine Language (ML), and Deep Learning are some of the most widely used tools in network management. AI helps us make better decisions about how to route traffic, analyze data, and monitor security while ML enables computers to analyze large data sets and make predictions about network usage and performance. Deep Learning further helps computers to recognize patterns in data and draw conclusions more accurately and quickly. In this presentation, we will discuss the use of these three tools in network management..

[Audio] Neural networks are a type of Artificial Intelligence and Machine Learning that uses mathematical algorithms and models to identify patterns in data. Each neural network is composed of multiple layers of neurons, all connected together to process the data and generate the desired output. This process allows the network to take an input, analyse it and produce the expected output..

[Audio] Neural networks are valuable tools for quickly testing and understanding what's going on with deep learning in your system. AI/ML-based network management allows the system to automatically select the best features, making it easier to get results with less time and effort. Thank you for your attention..