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AI in networks uses the power of artificial intelligence to enhance network performance by, for example, optimization or forecasting. This solution streamlines operations, employing advanced algorithms and machine learning for more efficient networking.

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[interview] The role of machine learning in modern network architecture and operations

While discussing modern network architecture and operations, machine learning (ML) integration promises enhanced efficiency, security, and adaptability. During our interview, Tomasz Janaszka, a seasoned Solution Architect, sheds light on the current state of ML in networking, its challenges, and its future direction.
Multiple authors

Multiple authors

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Long-term time series forecasting: an insight into the methods

In this article, we describe the use case and reasoning for long-term traffic forecasting, next we focus on the methods we used, and discuss alternative approaches and challenges to overcome.
Mariusz Budziński

Mariusz Budziński

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Introduction to time series for machine learning

Data is becoming an integral part of everyday life. Even if we are not fully aware of it, we deal with it all the time and it affects our lives in significant ways. This blog post elaborates on a particular type of data: time series. If you want to know more and discover what problems you can solve with time series analysis, you've come to the right place. Have you ever wondered how much data is created (including newly generated, captured, copied, or consumed data) each day? Currently it is 0.33 zettabytes every day!
Mariusz Budziński

Mariusz Budziński

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AI and Machine Learning for Networks: natural language processing and reinforcement learning

This is the third part of the series, where we focus on the next two classes of ML methods: natural language processing and reinforcement learning. Also, we outline the major challenges of applying various ideas for ML techniques to network problems. This part also summarizes all three parts of the blog post. The first part can be found here, and the second part can be found here. Natural language processing is a part of AI which allows computer programs to understand statements and words written in human language.
Tomasz Janaszka

Tomasz Janaszka

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AI and Machine Learning for Networks: classification, clustering and anomaly detection

This is the second article in the series AI/ML for networks. In this article we focus on the two classes of ML methods: classification and clustering. We also mention anomaly detection, which is an important topic in the context of network-related data processing where various classes of ML algorithms can be used. The first article of the series can be found here. In machine learning, classification is a supervised learning problem of identifying to which category an observation (or observations) belongs to (see Figure 1).
Tomasz Janaszka

Tomasz Janaszka

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AI and Machine Learning for Networks: time series forecasting and regression

Artificial intelligence (AI) and machine learning (ML) are trending topics in all technological domains. They offer a rich set of methods for data processing that can be used to solve practical problems, including those occurring in networks. We have prepared a series of articles to give you a better look at the various methods you can use for solving specific network issues. In a series of three articles, we present classes of AI/ML methods and algorithms that should play a key role in networking, considering the network/related data types they work on as well as specific types of problem they can help to solve.
Tomasz Janaszka

Tomasz Janaszka

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AI & machine learning for networks: example use cases

In today's digital age, the use of machine learning (ML) in networks has become increasingly prevalent. Modern businesses rely heavily on networks to maintain operations. However, it could be more and more challenging to manage network infrastructure effectively. One solution is to use machine learning (ML) algorithms to analyze network data and provide insights that can lead to more efficient network management. In this article, we will explore several ML use cases in network management including time series forecasting, capacity planning, intelligent alerting, and the use of external data to enable faster recovery of network components.
Karolina Rusinowicz

Karolina Rusinowicz

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AI and Machine Learning landscape for networks [infographic]

Aritifical intelligence (AI) and machine learning (ML) is a trending topic in all technological domains. It offers a rich set of methods for data processing that can be used to solve practical problems, including those occurring in networks. In this article we present a set of network issues for which the use of ML methods and different AI types can bring benefits and generally seems to make sense. Please note that this is a non-exhaustive list and is open to additions. For greater clarity, five main categories have been used.
Multiple authors

Multiple authors

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