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Data science involves the application of scientific methodologies, algorithms, and tools to extract knowledge and insights from data. This interdisciplinary field combines statistics, machine learning, and domain expertise to analyze large datasets and make data-driven decisions. Data science plays a crucial role in solving complex problems, predicting trends, and supporting evidence-based strategies.

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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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DATA

Data Science vs. Machine Learning: understanding the difference

We live in the so-called Zettabyte Era, which started in the middle of the 2010s when the amount of digital data and network traffic exceeded one zettabyte, or a trillion gigabytes. That might give you an idea just how much data is created and consumed nowadays. Not to mention that this amount grows at an increasing rate, and is projected to reach 181 zettabytes by 2025. Even though only a fraction of that data is stored for longer periods of time, the resulting data volume is still pretty intimidating.
Volha Duplenka

Volha Duplenka

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DATA

Types of Artificial Intelligence — a general overview of a formidable technology

We live in a world that even a couple of decades ago only science fiction writers could imagine. The most amazing innovations quickly become commonplace and normal. However, if there is still one that doesn’t fail to impress, that would be artificial intelligence, as proven by the recent burst of interest in ChatGPT, a new language model application with advanced AI features that can be used to build chatbots. We encounter AI everyday, not only in chatbots, but also in voice-activated personal assistants, self-driving cars, robot vacuum cleaners, image generation software, and many, many other instances.
Volha Duplenka

Volha Duplenka

Thumbnail of an article about Data Science vs. Data Analytics — main differences overview
DATA

Data Science vs. Data Analytics — main differences overview

We live in a world where data is ubiquitous. Websites track all their users’ every click. Your phone carries a map of where you are and where you’ve been. Smart homes record information about their occupants and sales sites collect data about your buying habits. More and more people want to look for usable information in the data, to draw practical conclusions. This interest in data has developed rapidly and widely with the consequence that companies are looking for professionals with data-driven skills to deal with specific data problems.
Katarzyna Hewelt

Katarzyna Hewelt

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DATA

Sentiment Analysis. What is it and how to use it?

During face-to-face conversations or by reading text - we, people, can determine a speaker's intention or mood, whether they feel happy about something or not. We can also identify the polarity of the context - is the expression positive or negative or maybe even neutral? It is relatively easy for most people, but do computers understand emotions? Is it important for machines to understand humans’ intentions? With the technological progression and advancement of machine learning techniques - our machines are getting closer and closer to answering these questions.
Katarzyna Hewelt

Katarzyna Hewelt

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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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